Image noise is unwanted information in an image
and can occur at any moment of time such as during image
capture, transmission, or processing and it may or may not
depend on image content. In order to remove the noise from
the noisy image, prior knowledge about the nature of noise
must be known otherwise noise removal causes the image
blurring. Identifying nature of noise is a challenging problem.
Many researchers have proposed their ideas on image noise
identification and each of the work has its assumptions,
advantages and limitations. In this paper, we proposed a new
methodology based on neural network for identifying the
different types of noise such as Non Gaussian, Gaussian white,
Salt and Pepper and Speckle noise.
This document provides an overview of image denoising techniques. It discusses different types of noise that can affect images, such as amplifier noise, impulsive noise, and speckle noise. It also describes various denoising methodologies, including spatial filtering techniques like mean and median filters, as well as transform domain filtering and wavelet thresholding. Spatial filters can smooth noise but also blur edges, while wavelet thresholding can preserve edges while removing noise. The document reviews noise models, denoising methods, and provides insights to determine the most effective approach based on the noise characteristics.
The document discusses preprocessing techniques for historical Sanskrit text documents before optical character recognition (OCR). It describes the basic steps of preprocessing which include scanning, noise removal through filtering techniques like mean, median and Wiener filters, and binarization. Newer techniques discussed are non-local means and total variation methods for noise removal, which help preserve details and edges while removing noise. The document evaluates the effect of different preprocessing filters and binarization on sample text images.
This document summarizes a research paper on efficient noise removal from images using a combination of non-local means filtering and wavelet packet thresholding of the method noise. It begins with an introduction to image denoising and an overview of common denoising methods. It then describes non-local means filtering and how it removes noise while preserving image details. However, at high noise levels, non-local means filtering can also blur some image details. The document proposes analyzing the method noise obtained from subtracting the non-local means filtered image from the noisy image. This method noise contains both noise and removed image details. Applying wavelet packet thresholding to the method noise can help recover some of the removed image details. The combined
The document compares several modern denoising algorithms for removing salt and pepper noise from images: the median filter, tolerance-based selective arithmetic mean filter technique (TSAMFT), and improved tolerance-based selective arithmetic mean filter technique (ITSAMFT) in 1 or 2 levels. It presents experimental results on the Lena test image corrupted with salt and pepper noise levels from 50% to 95%. The results show that Level-2 ITSAMFT performs best in maintaining high peak signal-to-noise ratio, correlation, image enhancement factor, and is most powerful at removing heavy salt and pepper noise, even at noise densities above 50% where other techniques begin to degrade.
This paper proposes a method for image denoising using wavelet thresholding while preserving edge information. It first detects edges in the noisy image using Canny edge detection. It then applies a wavelet transform and thresholds the coefficients, preserving values near detected edges. Two thresholding methods are discussed: Visushrink for sparse images and Sureshrink for others. The inverse wavelet transform is applied to obtain the denoised image with preserved edges. The goal is to remove noise while maintaining important image features like edges. The method is described to provide better denoising than alternatives that oversmooth edges.
A SURVEY : On Image Denoising and its Various TechniquesIRJET Journal
This document discusses various techniques for image denoising. It begins by defining different types of noise that can affect images, such as Gaussian noise, salt and pepper noise, and quantization noise. It then describes several denoising techniques, including linear filters like mean filters and non-linear filters like median filters. Adaptive filters are also discussed as being more selective than linear filters in preserving edges and high-frequency image components. The document concludes that no single denoising method works best for all images and that hybrid approaches combining multiple techniques may produce better results.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a fuzzy filter technique for removing random impulse noise from color video. The technique uses three successive filtering steps. In each step, fuzzy rules are used to detect noisy pixels in the color frame. Pixels detected as noisy are filtered, while other pixels remain unchanged, allowing for noise removal while preserving details. The technique is evaluated on video sequences by measuring the peak signal-to-noise ratio, mean absolute error, and normalized color difference between the original and filtered frames. Experimental results demonstrate the technique outperforms other filters like bilinear and bicubic interpolation filters.
This document provides an overview of image denoising techniques. It discusses different types of noise that can affect images, such as amplifier noise, impulsive noise, and speckle noise. It also describes various denoising methodologies, including spatial filtering techniques like mean and median filters, as well as transform domain filtering and wavelet thresholding. Spatial filters can smooth noise but also blur edges, while wavelet thresholding can preserve edges while removing noise. The document reviews noise models, denoising methods, and provides insights to determine the most effective approach based on the noise characteristics.
The document discusses preprocessing techniques for historical Sanskrit text documents before optical character recognition (OCR). It describes the basic steps of preprocessing which include scanning, noise removal through filtering techniques like mean, median and Wiener filters, and binarization. Newer techniques discussed are non-local means and total variation methods for noise removal, which help preserve details and edges while removing noise. The document evaluates the effect of different preprocessing filters and binarization on sample text images.
This document summarizes a research paper on efficient noise removal from images using a combination of non-local means filtering and wavelet packet thresholding of the method noise. It begins with an introduction to image denoising and an overview of common denoising methods. It then describes non-local means filtering and how it removes noise while preserving image details. However, at high noise levels, non-local means filtering can also blur some image details. The document proposes analyzing the method noise obtained from subtracting the non-local means filtered image from the noisy image. This method noise contains both noise and removed image details. Applying wavelet packet thresholding to the method noise can help recover some of the removed image details. The combined
The document compares several modern denoising algorithms for removing salt and pepper noise from images: the median filter, tolerance-based selective arithmetic mean filter technique (TSAMFT), and improved tolerance-based selective arithmetic mean filter technique (ITSAMFT) in 1 or 2 levels. It presents experimental results on the Lena test image corrupted with salt and pepper noise levels from 50% to 95%. The results show that Level-2 ITSAMFT performs best in maintaining high peak signal-to-noise ratio, correlation, image enhancement factor, and is most powerful at removing heavy salt and pepper noise, even at noise densities above 50% where other techniques begin to degrade.
This paper proposes a method for image denoising using wavelet thresholding while preserving edge information. It first detects edges in the noisy image using Canny edge detection. It then applies a wavelet transform and thresholds the coefficients, preserving values near detected edges. Two thresholding methods are discussed: Visushrink for sparse images and Sureshrink for others. The inverse wavelet transform is applied to obtain the denoised image with preserved edges. The goal is to remove noise while maintaining important image features like edges. The method is described to provide better denoising than alternatives that oversmooth edges.
A SURVEY : On Image Denoising and its Various TechniquesIRJET Journal
This document discusses various techniques for image denoising. It begins by defining different types of noise that can affect images, such as Gaussian noise, salt and pepper noise, and quantization noise. It then describes several denoising techniques, including linear filters like mean filters and non-linear filters like median filters. Adaptive filters are also discussed as being more selective than linear filters in preserving edges and high-frequency image components. The document concludes that no single denoising method works best for all images and that hybrid approaches combining multiple techniques may produce better results.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a fuzzy filter technique for removing random impulse noise from color video. The technique uses three successive filtering steps. In each step, fuzzy rules are used to detect noisy pixels in the color frame. Pixels detected as noisy are filtered, while other pixels remain unchanged, allowing for noise removal while preserving details. The technique is evaluated on video sequences by measuring the peak signal-to-noise ratio, mean absolute error, and normalized color difference between the original and filtered frames. Experimental results demonstrate the technique outperforms other filters like bilinear and bicubic interpolation filters.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
A Noncausal Linear Prediction Based Switching Median Filter for the Removal o...IDES Editor
In this paper, we propose a switching based median
filter for the removal of impulse noise, namely, the salt and
pepper noise in gray scale images. The filter is based on the
concept of substitution of noisy pixels prior to estimation. It
effectively suppresses the impulse noise in two stages. First,
the noisy pixels are detected by using the signal dependent
rank-ordered mean (SD-ROM) filter. In the second stage, the
noisy pixels are first substituted by the first order 2D
noncausal linear prediction technique and subsequently
replaced by the median value. Extensive simulations are
carried out to validate the proposed method. Experimental
results show improvements both visually and quantitatively
compared to other switching based median filters for the
removal of salt-and-pepper noise at different densities.
Abstract: Noise in an image is a serious problem In this
project, the various noise conditions are studied which are:
Additive white Gaussian noise (AWGN), Bipolar fixedvalued impulse noise, also called salt and pepper noise
(SPN), Random-valued impulse noise (RVIN), Mixed noise
(MN). Digital images are often corrupted by impulse noise
during the acquisition or transmission through
communication channels the developed filters are meant for
online and real-time applications. In this paper, the
following activities are taken up to draw the results: Study
of various impulse noise types and their effect on digital
images; Study and implementation of various efficient
nonlinear digital image filters available in the literature
and their relative performance comparison;
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
The document proposes a new filtering scheme based on partial differential equations (PDEs) to remove random-valued impulse noise from images. It introduces a new "edge, noisy, interior" (ENI) parameter to distinguish different pixel types. Based on ENI, it redefines controlling speed and fidelity functions for PDE models. New PDE models are presented using the controlling functions, allowing selective diffusion and fidelity processes. Experimental results on standard test images show the new PDE models outperform other methods like median filtering, adaptive center-weighted median, three-state median, Luo filter, and genetic programming in denoising ability, especially at higher noise levels.
Image Restoration Using Particle Filters By Improving The Scale Of Texture Wi...CSCJournals
Traditional techniques are based on restoring image values based on local smoothness constraints within fixed bandwidth windows where image structure is not considered. Common problem for such methods is how to choose the most appropriate bandwidth and the most suitable set of neighboring pixels to guide the reconstruction process. The present work proposes a denoising technique based on particle filtering using MRF (Markov Random Field). It is an automatic technique to capture the scale of texture. The contribution of our method is the selection of an appropriate window in the image domain. For this we first construct a set containing all occurrences then the conditional pdf can be estimated with a histogram of all center pixel values. Particle evolution is controlled by the image structure leading to a filtering window adapted to the image content. Our method explores multiple neighbors’ sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypothesis according to its relevance and its contribution in the denoising process.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
Comparison of Denoising Filters on Greyscale TEM Image for Different NoiseIOSR Journals
This document compares five different filters for removing noise from transmission electron microscopy (TEM) images: Wiener filter using discrete wavelet transform, hybrid median filter, bilateral filter, dual vectorial ROF filter, and fuzzy histogram equalization. Four types of noise are added to TEM images at varying levels: Gaussian noise, speckle noise, salt and pepper noise, and Poisson noise. The filters are applied to the noisy images and evaluated based on mean, mean square error, signal-to-noise ratio, and peak signal-to-noise ratio. Simulation results show that the dual vectorial ROF filter performs the best according to the evaluation metrics for each type of noise.
This document summarizes a research paper that proposes a new method for reducing noise in digital images using curvelet transformation with Log Gabor filtering. It begins by introducing common sources of noise in digital images and existing denoising methods. It then describes curvelet transformation and Log Gabor filtering in more detail. The proposed method decomposes a noisy image into wavelets, applies curvelet transformation with Log Gabor filtering to attenuate color frequencies, and then reconstructs the image. The document presents this methodology and compares the denoised image quality to other methods using peak signal-to-noise ratio (PSNR). Experimental results showed that the proposed curvelet transformation with Log Gabor filtering produces higher PSNR values and less visual artifacts
Performance Analysis and Optimization of Nonlinear Image Restoration Techniqu...CSCJournals
Abstract: This paper is concerned with critical performance analysis of spatial nonlinear restoration techniques for continuous tone images from various fields (Medical images, Natural images, and others images).The performance of the nonlinear restoration methods is provided with possible combination of various additive noises and images from diversified fields. Efficiency of nonlinear restoration techniques according to difference distortion and correlation distortion metrics is computed.Tests performed on monochrome images, with various synthetic and real-life degradations, without and with noise, in single frame scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio(ISNR) measure. The comparison of the present approach with previous individual methods in terms of mean square error, peak signal-to-noise ratio, and normalised absolute error is also provided. In comparisons with other state of art methods, our approach yields better to optimization, and shows to be applicable to a much wider range of noises. We discuss how experimental results are useful to guide to select the effective combination. Promising performance analysed through computer simulation and compared to give critical analysis.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
This document summarizes research on using different filters to reduce noise in digital images. It begins by introducing digital image processing and its advantages over analog processing. Then it discusses three types of filters:
1) Mean filter, which replaces each pixel value with the average of neighboring pixels, reducing local variations caused by noise.
2) Median filter, a nonlinear filter that replaces each pixel with the median of neighboring pixels, preserving edges while removing noise.
3) Wiener filter, an optimal linear filter that minimizes the mean square error between the estimated image and the original. It operates by weighting each pixel by the power spectral density of the noise and original image.
The document then evaluates these filters for removing
An Efficient Image Denoising Approach for the Recovery of Impulse NoisejournalBEEI
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Removal of noise is a determining track in
the image rebuilding process, but denoising of image remains a
claiming problem in upcoming analysis accomplice along
image processing. Denoising is utilized to expel the noise from
corrupted image, where as we need to maintain the edges and
other detailed characteristics almost accessible. This noise gets
imported during accretion, transmitting & receiving and
storage & retrieval techniques. In this paper, to discover out
denoised image the modified denoising technique and the local
adaptive wavelet image denoising technique can be obtained.
The input (noisy image) is denoised with the help of modified
denoising technique which is form on wavelet domain as well as
spatial domain along with the local adaptive wavelet image
denoising technique which is form on wavelet domain. In this
paper, I have appraised and analyzed achievements of
modified denoising technique and the local adaptive wavelet
image denoising technique. The above procedures are
contemplated with other based on PSNR between input image
and noisy image and SNR between input image and denoised
image. Simulation and experimental outgrowth for an image
reflects as the mean square error of the local adaptive wavelet
image denoising procedure is less efficient as compare to
modified denoising procedure including the signal to noise
ratio of the local adaptive wavelet image denoising technique is
effective than other approach. Therefore, the image after
denoising has a superior visual effect. In this paper, these two
techniques are materialized with the help of MATLAB for
denoising of image
User Controlled Privacy in Participatory SensingIDES Editor
This document describes a proposed model for collaborative infrastructure monitoring using participatory sensing. The model involves:
1) Individual sensor networks deployed in homes/buildings that are connected to a cluster head. Cluster heads share some data with a central base station.
2) Users have control over what data they share and at what level of granularity. They can authorize other users to access their data at different levels.
3) Trust between users is calculated based on their past interactions, to determine how much data one user will share with others. If no past interactions exist, opinions from other trusted users can be used to establish an initial trust value.
4) The goal is to integrate individual sensor networks to monitor infrastructure
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Ne...IDES Editor
A reliable routing protocol for wireless sensor
networks (WSN) should be capable of adjusting to
constantly varying network conditions while conserving
maximum power. Existing Routing protocols provide
reliability at the cost of high energy consumption. In this
paper, we propose to develop an Adaptive Energy Efficient
Reliable Routing Protocol (AEERRP) with the aim of
keeping the energy consumption low while achieving high
reliability. In our proposed protocol, the data forwarding
probability is adaptively adjusted based on the measured
loss conditions at the sink. So only for high loss rates, a node
makes use of high transmission power to arrive at the sink.
Whenever the loss rate is low, it adaptively lessens the
transmission power. Since the source rebroadcasts the data,
until the packet loss is minimized, high data reliability is
achieved. By simulation results we show that the proposed
protocol achieves high reliability while ensuring low energy
consumption and overhead.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
A Noncausal Linear Prediction Based Switching Median Filter for the Removal o...IDES Editor
In this paper, we propose a switching based median
filter for the removal of impulse noise, namely, the salt and
pepper noise in gray scale images. The filter is based on the
concept of substitution of noisy pixels prior to estimation. It
effectively suppresses the impulse noise in two stages. First,
the noisy pixels are detected by using the signal dependent
rank-ordered mean (SD-ROM) filter. In the second stage, the
noisy pixels are first substituted by the first order 2D
noncausal linear prediction technique and subsequently
replaced by the median value. Extensive simulations are
carried out to validate the proposed method. Experimental
results show improvements both visually and quantitatively
compared to other switching based median filters for the
removal of salt-and-pepper noise at different densities.
Abstract: Noise in an image is a serious problem In this
project, the various noise conditions are studied which are:
Additive white Gaussian noise (AWGN), Bipolar fixedvalued impulse noise, also called salt and pepper noise
(SPN), Random-valued impulse noise (RVIN), Mixed noise
(MN). Digital images are often corrupted by impulse noise
during the acquisition or transmission through
communication channels the developed filters are meant for
online and real-time applications. In this paper, the
following activities are taken up to draw the results: Study
of various impulse noise types and their effect on digital
images; Study and implementation of various efficient
nonlinear digital image filters available in the literature
and their relative performance comparison;
This document describes an image denoising technique called the TWIST (Transform With Iterative Sampling and Thresholding) method. It begins with background on common types of image noise like Gaussian, salt-and-pepper, and quantization noise. It then discusses related work using eigendecomposition and the Nystrom extension for denoising. The proposed TWIST method uses the Nystrom extension to approximate the filter matrix with a low-rank matrix, allowing efficient processing of the entire image. It performs eigendecomposition on sample pixels to estimate eigenvalues and eigenvectors, then iterates this process with thresholding to denoise the image while preserving edges.
The document proposes a new filtering scheme based on partial differential equations (PDEs) to remove random-valued impulse noise from images. It introduces a new "edge, noisy, interior" (ENI) parameter to distinguish different pixel types. Based on ENI, it redefines controlling speed and fidelity functions for PDE models. New PDE models are presented using the controlling functions, allowing selective diffusion and fidelity processes. Experimental results on standard test images show the new PDE models outperform other methods like median filtering, adaptive center-weighted median, three-state median, Luo filter, and genetic programming in denoising ability, especially at higher noise levels.
Image Restoration Using Particle Filters By Improving The Scale Of Texture Wi...CSCJournals
Traditional techniques are based on restoring image values based on local smoothness constraints within fixed bandwidth windows where image structure is not considered. Common problem for such methods is how to choose the most appropriate bandwidth and the most suitable set of neighboring pixels to guide the reconstruction process. The present work proposes a denoising technique based on particle filtering using MRF (Markov Random Field). It is an automatic technique to capture the scale of texture. The contribution of our method is the selection of an appropriate window in the image domain. For this we first construct a set containing all occurrences then the conditional pdf can be estimated with a histogram of all center pixel values. Particle evolution is controlled by the image structure leading to a filtering window adapted to the image content. Our method explores multiple neighbors’ sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypothesis according to its relevance and its contribution in the denoising process.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
Comparison of Denoising Filters on Greyscale TEM Image for Different NoiseIOSR Journals
This document compares five different filters for removing noise from transmission electron microscopy (TEM) images: Wiener filter using discrete wavelet transform, hybrid median filter, bilateral filter, dual vectorial ROF filter, and fuzzy histogram equalization. Four types of noise are added to TEM images at varying levels: Gaussian noise, speckle noise, salt and pepper noise, and Poisson noise. The filters are applied to the noisy images and evaluated based on mean, mean square error, signal-to-noise ratio, and peak signal-to-noise ratio. Simulation results show that the dual vectorial ROF filter performs the best according to the evaluation metrics for each type of noise.
This document summarizes a research paper that proposes a new method for reducing noise in digital images using curvelet transformation with Log Gabor filtering. It begins by introducing common sources of noise in digital images and existing denoising methods. It then describes curvelet transformation and Log Gabor filtering in more detail. The proposed method decomposes a noisy image into wavelets, applies curvelet transformation with Log Gabor filtering to attenuate color frequencies, and then reconstructs the image. The document presents this methodology and compares the denoised image quality to other methods using peak signal-to-noise ratio (PSNR). Experimental results showed that the proposed curvelet transformation with Log Gabor filtering produces higher PSNR values and less visual artifacts
Performance Analysis and Optimization of Nonlinear Image Restoration Techniqu...CSCJournals
Abstract: This paper is concerned with critical performance analysis of spatial nonlinear restoration techniques for continuous tone images from various fields (Medical images, Natural images, and others images).The performance of the nonlinear restoration methods is provided with possible combination of various additive noises and images from diversified fields. Efficiency of nonlinear restoration techniques according to difference distortion and correlation distortion metrics is computed.Tests performed on monochrome images, with various synthetic and real-life degradations, without and with noise, in single frame scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio(ISNR) measure. The comparison of the present approach with previous individual methods in terms of mean square error, peak signal-to-noise ratio, and normalised absolute error is also provided. In comparisons with other state of art methods, our approach yields better to optimization, and shows to be applicable to a much wider range of noises. We discuss how experimental results are useful to guide to select the effective combination. Promising performance analysed through computer simulation and compared to give critical analysis.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
This document summarizes research on using different filters to reduce noise in digital images. It begins by introducing digital image processing and its advantages over analog processing. Then it discusses three types of filters:
1) Mean filter, which replaces each pixel value with the average of neighboring pixels, reducing local variations caused by noise.
2) Median filter, a nonlinear filter that replaces each pixel with the median of neighboring pixels, preserving edges while removing noise.
3) Wiener filter, an optimal linear filter that minimizes the mean square error between the estimated image and the original. It operates by weighting each pixel by the power spectral density of the noise and original image.
The document then evaluates these filters for removing
An Efficient Image Denoising Approach for the Recovery of Impulse NoisejournalBEEI
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Removal of noise is a determining track in
the image rebuilding process, but denoising of image remains a
claiming problem in upcoming analysis accomplice along
image processing. Denoising is utilized to expel the noise from
corrupted image, where as we need to maintain the edges and
other detailed characteristics almost accessible. This noise gets
imported during accretion, transmitting & receiving and
storage & retrieval techniques. In this paper, to discover out
denoised image the modified denoising technique and the local
adaptive wavelet image denoising technique can be obtained.
The input (noisy image) is denoised with the help of modified
denoising technique which is form on wavelet domain as well as
spatial domain along with the local adaptive wavelet image
denoising technique which is form on wavelet domain. In this
paper, I have appraised and analyzed achievements of
modified denoising technique and the local adaptive wavelet
image denoising technique. The above procedures are
contemplated with other based on PSNR between input image
and noisy image and SNR between input image and denoised
image. Simulation and experimental outgrowth for an image
reflects as the mean square error of the local adaptive wavelet
image denoising procedure is less efficient as compare to
modified denoising procedure including the signal to noise
ratio of the local adaptive wavelet image denoising technique is
effective than other approach. Therefore, the image after
denoising has a superior visual effect. In this paper, these two
techniques are materialized with the help of MATLAB for
denoising of image
User Controlled Privacy in Participatory SensingIDES Editor
This document describes a proposed model for collaborative infrastructure monitoring using participatory sensing. The model involves:
1) Individual sensor networks deployed in homes/buildings that are connected to a cluster head. Cluster heads share some data with a central base station.
2) Users have control over what data they share and at what level of granularity. They can authorize other users to access their data at different levels.
3) Trust between users is calculated based on their past interactions, to determine how much data one user will share with others. If no past interactions exist, opinions from other trusted users can be used to establish an initial trust value.
4) The goal is to integrate individual sensor networks to monitor infrastructure
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Ne...IDES Editor
A reliable routing protocol for wireless sensor
networks (WSN) should be capable of adjusting to
constantly varying network conditions while conserving
maximum power. Existing Routing protocols provide
reliability at the cost of high energy consumption. In this
paper, we propose to develop an Adaptive Energy Efficient
Reliable Routing Protocol (AEERRP) with the aim of
keeping the energy consumption low while achieving high
reliability. In our proposed protocol, the data forwarding
probability is adaptively adjusted based on the measured
loss conditions at the sink. So only for high loss rates, a node
makes use of high transmission power to arrive at the sink.
Whenever the loss rate is low, it adaptively lessens the
transmission power. Since the source rebroadcasts the data,
until the packet loss is minimized, high data reliability is
achieved. By simulation results we show that the proposed
protocol achieves high reliability while ensuring low energy
consumption and overhead.
Inverse Gamma Distribution based Delay and Slew Modeling for On- Chip VLSI RC...IDES Editor
The Elmore delay is fast becoming ineffective for
deep submicron technologies, and reduced order transfer
function delays are impractical for use as early-phase design
metrics or as design optimization cost functions. This paper
describes an accurate approach for fitting moments of the
impulse response to probability density functions so that delay
and slew metric can be estimated accurately at an early
physical design stage. PERI (Probability distribution function
Extension for Ramp Inputs) technique has been used that
extends the delay and slew metrics for step inputs to the more
general and realistic non-step or ramp inputs. The accuracy
of the proposed model is justified by the results obtained from
the proposed model and that of SPICE simulations
Depth-Image-based Facial Analysis between Age Groups and Recognition of 3D FacesIDES Editor
Face recognition is still an open problem. Many 2D
face recognition approaches came into light to achieve high
recognition rate. But these approaches are still challenged by
the changes in illuminations, expressions, pose, noise, etc. A
3D face recognition technique is proposed to overcome such
challenges and to enhance robustness to expression variations.
Here, we compare the person at different age groups with
higher recognition rate in comparison to 2D face recognition
techniques. We propose a two stage procedure of 3D face
recognition based on FLD (Fisher Linear Discriminant), SURF
operator and depth-image. First, FLD is used on depth-image
to perform recognition and then the SURF features of 2D
gray images to carry out the refined recognition. Finally, our
proposed work will increase the robustness in expression
variations.
An Information Maximization approach of ICA for Gender ClassificationIDES Editor
In this paper, a novel and successful method for
gender classification from human faces using dimensionality
reduction technique is proposed. Independent Component
Analysis (ICA) is one of such techniques. In the current
scheme, a thrust is given on the different algorithms and
architectures of ICA. An information maximization ICA is
discussed with its two architecture and compared with the two
architectures of fast ICA. Support Vector Machine (SVM) is
used as a classifier for the separation of male and female
classes. All experiments are done on FERET database. Results
are obtained for the different combinations of train and test
database sizes. For larger
training set SVM is performing with an accuracy of 98%. The
accuracy values are varied for change in size of testing set and
the proposed system performs with an average accuracy of
96%. An improvement in performance is achieved using class
discriminability which performs with 100% accuracy.
Energy efficient reliable routing considering residual energy in wireless ad ...LeMeniz Infotech
Energy Efficient Reliable Routing Considering Residual Energy in Wireless Ad Hoc Networks
A model for the energy consumption of a node as a function of its throughput in a wireless CSMA network is proposed. A single-hop network is modeled, and then a multi-hop network
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
This document describes a two-stage technique for removing impulse noise from digital images using neural networks and fuzzy logic. In the first stage, a neural network is used to detect and remove noise from the image cleanly while preserving image details. In the second stage, fuzzy decision rules inspired by the human visual system are used to enhance image quality by compensating for blurring or destruction caused in the first stage. The goal is to remove noise cleanly without blurring edges while enhancing the overall visual quality of the processed image.
noise remove in image processing by fuzzy logicRucku
This document summarizes a research paper that proposes a two-stage technique for removing impulse noise from digital images using neural networks and fuzzy logic. In the first stage, a neural network is used to detect and remove noise while preserving image details. In the second stage, fuzzy decision rules inspired by the human visual system are used to further process pixels and enhance image quality, especially in sensitive regions. The technique aims to remove noise cleanly without blurring edges or destroying important information. It is presented as an improvement over conventional noise removal methods.
Abstract: These days analysing patient data in the form of medical images to perform diagnose while doing detection
and prediction of a disease has emerged as a biggest research challenge. All these medical images can be in the form of
X-RAY, CT scan, MRI, PET and SPECT. These images carry minute information about heart, brain, nerves etc within
themselves. It may happen that these images get corrupted due to noise while capturing them. This makes the complete
image interpretation process very difficult and inaccurate. It has been found that the accuracy rate of existing method is
very less so improvement is required to make them more accurate. This paper proposes a Machine Learning Model based
on Convolutional Neural Network (CNN) that will contain all the filters required to de-noise MRI or USI Images. This
model will have same error rate efficiency like those of data mining techniques which radiologists were interested in. The
filters used in the proposed work are namely Weiner Filter, Gaussian Filter, Median Filter that are capable of removing
most common noises such as Salt and Pepper, Poisson, Speckle, Blurred, Gaussian existing in MRI images in Grey Scale
and RGB Scale.
Keywords: Convolution Neural Network, Denoising, Machine Learning, Deep Learning, Image Noise, Filters
An automated technique for image noise identification using a simple pattern ...Yixin Chen
This document proposes and evaluates a simple technique for identifying image noise types using pattern classification. It involves isolating representative noise samples from a noisy image using filters, extracting statistical features like kurtosis and skewness, and classifying the noise type based on similarity to known noise classes. The technique is tested on images with Gaussian white, uniform white, salt-and-pepper, and speckle noise. Experimental results show the technique can accurately identify single or mixed noise types based on comparing statistical features of isolated noise samples to expected reference values for each noise class.
Strengthen Fuzzy Pronouncement for Impulse Noise Riddance Method for Images B...IRJET Journal
This document proposes a 4-stage neural network method for removing impulse noise from images. It begins with a 1st stage additive neural network to cleanly remove noise while preserving uncorrupted data. The 2nd stage uses fuzzy decision rules inspired by the human visual system to compensate for blurring and details lost from median filtering. A 3rd neural network is then used to enhance regions determined by the fuzzy rules to have higher visual importance. The goal is to remove impulse noise cleanly without blurring edges, by dividing the method into noise removal and subsequent image enhancement stages using neural networks and fuzzy logic.
This document summarizes an article that proposes an adaptive nonlinear filtering technique for image restoration. It begins by discussing common types of image noise and degradation models. It then discusses existing median filtering and adaptive filtering techniques that aim to remove noise while preserving edges. The paper proposes a new adaptive length median/mean algorithm that can simultaneously remove noise artifacts like impulses, strip lines, drop lines, band missing, and blotches. It detects corrupted pixels and evaluates new pixels to replace them. The algorithm switches between median and mean filtering depending on noise levels to better preserve details. The performance of the algorithm is evaluated based on metrics like mean square error and peak signal-to-noise ratio. The algorithm is found to outperform standard techniques in
1. The document discusses efficient analysis of medical image de-noising for MRI and ultrasound images. It investigates three filters: median, Gaussian, and Wiener filters.
2. It provides background on noise in medical images and summarizes previous research on de-noising algorithms for different image modalities.
3. The mathematical background section explains how the median, Gaussian, and Wiener filters work for noise removal. It also defines peak signal-to-noise ratio (PSNR) to evaluate de-noising outcomes.
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
The document reviews denoising techniques for additive white Gaussian noise (AWGN) introduced in stationary images. It discusses various noise models including Gaussian, salt and pepper, speckle, and Brownian noise. It also reviews common denoising approaches such as linear and nonlinear filtering, mean filtering, least mean square adaptive filtering, median filtering, and discrete wavelet transform. The discrete wavelet transform approach is effective for denoising images corrupted with Gaussian noise, while median and mean filtering work better for salt and pepper noise removal. The selection of denoising algorithm depends on the type of noise present in the image.
An adaptive method for noise removal from real world imagesIAEME Publication
The document summarizes an adaptive method for noise removal from real world images. It proposes modifying the bilateral filter, which considers both spatial and intensity distances between pixels. The modified filter adapts its strength based on the local noise level in the image. It estimates the smoothing parameter by analyzing noise strength factors within blocks of different sizes. This helps determine the appropriate block size to use for a given image region. The filter aims to remove Gaussian noise while preserving edges and details to enhance image quality. Experimental results show it performs well across different images for a wide range of noise levels.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Removal of Gaussian Impulsive Noise from Computed TomographyIRJET Journal
This document presents a new filtering algorithm to remove mixed Gaussian and impulse noise from computed tomography (CT) images. CT images are often corrupted by noise during acquisition, which reduces image quality. Existing filters are not very effective at removing both types of noise. The proposed hybrid filter separates image pixels into corrupted and non-corrupted groups. It then applies Gaussian smoothing and median filtering to the groups based on their noise characteristics to remove the noise. When tested on 10 CT images, the algorithm improved peak signal-to-noise ratio over existing filters, indicating better noise removal and higher quality output images.
An overview of the fundamental approaches that yield several image denoising ...TELKOMNIKA JOURNAL
This document provides an overview of fundamental approaches to image denoising. It discusses spatial domain filtering methods like mean and median filters that operate directly on pixel values. It also covers transform domain methods that convert the image to another domain like frequency before filtering, using tools like wavelets, curvelets and BM3D. Hybrid methods combining spatial and transform techniques are also presented. The document aims to introduce these approaches to facilitate further research on solving noise problems in images.
1) The document discusses a technique for detecting bone fractures in x-ray images using edge detection methods like Gaussian and Canny edge detection.
2) It involves preprocessing the x-ray image, applying Gaussian filtering to remove noise, using Canny edge detection to identify edges, and inverting the image to make fractures more visible.
3) The method is implemented using the AForge library and is found to accurately detect bone fractures in x-ray images for use in medical applications like aiding doctors' diagnoses.
This document summarizes a student's analysis of different image filtering techniques to reduce noise. It outlines the objective to compare mean, median, adaptive median, and bilateral filters. It introduces various types of image noise like salt and pepper, Gaussian, and speckle noise. Performance is analyzed using PSNR scores. Adaptive median filtering achieved the best results for salt and pepper noise below 0.5 density and Gaussian noise. Average filtering worked best for speckle noise, but frequency domain filters are needed to significantly reduce speckle noise. PSNR is limited and SSIM would provide a better quality assessment.
Dissertation synopsis for imagedenoising(noise reduction )using non local me...Arti Singh
Dissertation report for image denoising using non-local mean algorithm, discussion about subproblem of noise reduction,descrption for problem in image noise
This research focus on image sharpness and quality
using a self-organizing migration algorithm (SOMA) with
curvelet based nonlocal means (CNLM) denoising is presented.
In this paper, first transform curvelet is using on the noisy image
obtain image. Find the comparison of 2 pixels in the noisy picture
which is evaluated depend on these curvelet produced pictures
which include complementary picture capabilities at particularly
excessive noise levels and the noisy picture at especially low noise
levels. Then pixel comparison and noisy photograph are used to
denoised end outcome found applying NLM technique. SOMA
obtains better quality with the aid of varying threshold on the
basis of image pixels. The threshold can be determined using
lower and upper value of noisy image. Quantitative evaluations
illustrate that the proposed scheme perform more enhanced than
the other filters namely median filter (MF) progressive switching
median filter (PSMF), NLM, CNLM denoising process in
conditions of noise removal and detail protection. Using different
parameters for example Peak Signal Noise Ratio (PSNR), means
Structural Similarity Matrix (MSSIM) and SSIM for noise free
image. It is illustrated that the improved scheme provides an
excessive degree of noise removal whilst maintaining the edges
and other information in the image. In this study, algorithm is
tested on dissimilar kind of noise explicitly, Random Valued
Impulse Noise (RVIN), Gaussian Noise and Salt and Pepper
(SNP) Noise with varying noise density from 10 to 90%. The
proposed system proves better performance on high noise
density.
Noise Level Estimation for Digital Images Using Local Statistics and Its Appl...TELKOMNIKA JOURNAL
In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.
Similar to Neural Network Based Noise Identification in Digital Images (20)
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Mental Stress Evaluation using an Adaptive ModelIDES Editor
Chronic stress can have serious physiological and
psychological impact on an individual’s health. Wearable
sensor systems can enable physicians to monitor physiological
variables and observe the impact of stress over long periods of
time. To correlate an individual’s physiological measures with
their perception of psychological stress, it is essential that
the stress monitoring system accounts for individual
differences in self-reporting. Self-reporting of stress is highly
subjective as it is dependent on an individual’s perception of
stress and thus prone to errors. In addition, subjects can tailor
their answers to present their behavior more favorably. In
this paper we present an adaptive model which allows recorded
stress scores and physiological variables to be tuned to remove
biases in self-reported scores. The model takes an individual’s
physiological and psychological responses into account and
adapts to the user’s variations. Using our adaptive model,
physiological data is mapped efficiently to perceived stress
levels with 90% accuracy.
Genetic Algorithm based Mosaic Image Steganography for Enhanced SecurityIDES Editor
The document summarizes previous work on mosaic image steganography and proposes using genetic algorithms and key-based random permutation to improve the technique. Mosaic image steganography hides a secret image by dividing it into fragments and embedding the fragments into a target image to create a mosaic. Previous methods required a large database of images or allowed only arbitrary target image selection. The proposed method uses genetic algorithms to generate a mapping sequence for embedding tile images without a database, improving clarity and reducing computational complexity. It also applies a key-based random permutation to the mapping sequence for enhanced security and robustness. The mosaic image can be recovered using the same key and mapping sequence, making it a lossless data hiding method.
3-D FFT Moving Object Signatures for Velocity FilteringIDES Editor
In this paper a bank of velocity filters is devised to
be used for isolating a moving object with specific velocity
(amplitude and direction) in a sequence of frames. The
approach used is a 3-D FFT based experimental procedure
without applying any theoretical concept from velocity filters.
Accordingly, each velocity filter is built using the spectral
signature of an object moving with specific velocity.
Experimentation reveals the capabilities of the constructed
filter bank to separate moving objects as far as the amplitude
as well as the direction of the velocity are concerned.
Accordingly, weak objects can be detected when moving with
different velocity close to strong vehicles. Accelerating objects
can be detected only on the part of their trajectory they have
the specific velocity. Problems which arise due to the
discontinuities at the edges of the frame sequences are
discussed.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations