Masters defense for Parallel Processing for Digital Image Enhancement
By Nora Youssef
Defense: 1 Oct 2015
Judges:
Prof. Dr. El-Sayed M. El-Horbaty - ASU
Prof. Dr. Hani Mohamed Kamal Mahdi - ASU
Prof. Dr. Mohamed Waheed Meselhey - Suez Channel University
Image Resolution Enhancement using DWT and Spatial Domain Interpolation Techn...IJERA Editor
Image Resolution is one of the important quality metrics of images. Images with high resolution are required in
many fields. In this paper, a new resolution enhancement technique is proposed based on the interpolation of
four sub band images generated by Discrete Wavelet Transform (DWT) and the original Low Resolution (LR)
input image. In this technique, the four sub band images generated by DWT and the input LR image are
interpolated with scaling factor, α and then performed inverse DWT to obtain the intermediate High Resolution
(HR) Image. The difference between the intermediate HR image and the interpolated LR input image is added
to the intermediate HR image to obtain final output HR Image. Lanczos interpolation is used in this technique.
The proposed technique is tested on well known bench mark images. The quantitative and visual results shows
the superiority of the proposed technique over the conventional and state of art image resolution enhancement
techniques in wavelet domain using haar wavelet filter.
Image processing involves analyzing, manipulating, and storing digital images using computer software. It includes tasks like cropping, resizing, adjusting contrast and applying filters to images. Image processing has applications in fields like television, medicine, surveillance and more. It involves areas like acquisition, enhancement, restoration, compression and recognition. Image restoration aims to improve degraded images back to their original state. Noise removal is an important aspect of restoration, and common types of noise include Gaussian and impulse noise. Various filters can be used for noise removal, such as mean, median and advanced algorithms.
Digital image processing - Image Enhancement (MATERIAL)Mathankumar S
This document discusses various image enhancement techniques including contrast stretching, compression of dynamic range, histogram equalization, and histogram specification. It provides definitions and explanations of these concepts with examples. Histogram equalization aims to produce a linear histogram to enhance an image, while histogram specification allows specifying a desired output histogram. Local enhancement can be achieved by applying these histogram processing methods over small non-overlapping regions instead of globally to reduce edge effects.
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
Comparative study of Salt & Pepper filters and Gaussian filtersAnkush Srivastava
The document compares Salt & Pepper noise and Gaussian noise removal methods. It discusses various filtering techniques like minimum, maximum, mean, median, and rank order filters. For Salt & Pepper noise, minimum and maximum filters remove white and black pixels respectively, while mean, median, and rank order filters replace pixel values with local statistics. The document proposes two additional methods that calculate the mean of middle pixel values after sorting. It analyzes noise removal using these filters and concludes median filtering best reduces Salt & Pepper noise.
This document proposes a new method called the improved trimmed mean median filter for removing fixed valued impulse noise from gray scale images. The method uses a novel combination of mean, median, and trimmed values to eliminate salt and pepper noise while preserving image details like edges. The method is tested on images like Mandrill and Lena and is shown to outperform other filters like the standard median filter, decision based median filter, and modified decision based median filter in terms of peak signal to noise ratio and mean square error values, with better visual quality. The goal of the proposed method is to not only improve peak signal to noise ratio but also improve visual perception and reduce image blurring compared to other filters.
This document discusses image de-noising techniques for salt and pepper noise. It proposes a new robust mean filter method that aims to improve peak signal-to-noise ratio, visual perception, and reduce image blurring compared to other filters like standard median, decision based median, and modified decision based median filters. The proposed algorithm replaces noisy pixels with the trimmed mean value of neighboring pixels while preserving important image details. Experimental results on test images show the proposed method achieves better peak signal-to-noise ratio, mean square error, and mean absolute error values with better visual quality and human perception than other methods.
Image Resolution Enhancement using DWT and Spatial Domain Interpolation Techn...IJERA Editor
Image Resolution is one of the important quality metrics of images. Images with high resolution are required in
many fields. In this paper, a new resolution enhancement technique is proposed based on the interpolation of
four sub band images generated by Discrete Wavelet Transform (DWT) and the original Low Resolution (LR)
input image. In this technique, the four sub band images generated by DWT and the input LR image are
interpolated with scaling factor, α and then performed inverse DWT to obtain the intermediate High Resolution
(HR) Image. The difference between the intermediate HR image and the interpolated LR input image is added
to the intermediate HR image to obtain final output HR Image. Lanczos interpolation is used in this technique.
The proposed technique is tested on well known bench mark images. The quantitative and visual results shows
the superiority of the proposed technique over the conventional and state of art image resolution enhancement
techniques in wavelet domain using haar wavelet filter.
Image processing involves analyzing, manipulating, and storing digital images using computer software. It includes tasks like cropping, resizing, adjusting contrast and applying filters to images. Image processing has applications in fields like television, medicine, surveillance and more. It involves areas like acquisition, enhancement, restoration, compression and recognition. Image restoration aims to improve degraded images back to their original state. Noise removal is an important aspect of restoration, and common types of noise include Gaussian and impulse noise. Various filters can be used for noise removal, such as mean, median and advanced algorithms.
Digital image processing - Image Enhancement (MATERIAL)Mathankumar S
This document discusses various image enhancement techniques including contrast stretching, compression of dynamic range, histogram equalization, and histogram specification. It provides definitions and explanations of these concepts with examples. Histogram equalization aims to produce a linear histogram to enhance an image, while histogram specification allows specifying a desired output histogram. Local enhancement can be achieved by applying these histogram processing methods over small non-overlapping regions instead of globally to reduce edge effects.
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
Comparative study of Salt & Pepper filters and Gaussian filtersAnkush Srivastava
The document compares Salt & Pepper noise and Gaussian noise removal methods. It discusses various filtering techniques like minimum, maximum, mean, median, and rank order filters. For Salt & Pepper noise, minimum and maximum filters remove white and black pixels respectively, while mean, median, and rank order filters replace pixel values with local statistics. The document proposes two additional methods that calculate the mean of middle pixel values after sorting. It analyzes noise removal using these filters and concludes median filtering best reduces Salt & Pepper noise.
This document proposes a new method called the improved trimmed mean median filter for removing fixed valued impulse noise from gray scale images. The method uses a novel combination of mean, median, and trimmed values to eliminate salt and pepper noise while preserving image details like edges. The method is tested on images like Mandrill and Lena and is shown to outperform other filters like the standard median filter, decision based median filter, and modified decision based median filter in terms of peak signal to noise ratio and mean square error values, with better visual quality. The goal of the proposed method is to not only improve peak signal to noise ratio but also improve visual perception and reduce image blurring compared to other filters.
This document discusses image de-noising techniques for salt and pepper noise. It proposes a new robust mean filter method that aims to improve peak signal-to-noise ratio, visual perception, and reduce image blurring compared to other filters like standard median, decision based median, and modified decision based median filters. The proposed algorithm replaces noisy pixels with the trimmed mean value of neighboring pixels while preserving important image details. Experimental results on test images show the proposed method achieves better peak signal-to-noise ratio, mean square error, and mean absolute error values with better visual quality and human perception than other methods.
This document discusses image restoration techniques for images degraded by space-variant blurs. It describes running sinusoidal transforms as a method for space-variant image restoration. Running transforms involve applying a short-time orthogonal transform within a moving window, allowing approximately stationary processing. This addresses limitations of methods that assume space-invariance or require coordinate transformations. The chapter presents running discrete sinusoidal transforms as a way to perform the space-variant restoration by modifying orthogonal transform coefficients within the window to estimate pixel values.
Speckle Noise Reduction in Ultrasound Images using Adaptive and Anisotropic D...Md. Shohel Rana
US Imaging Technique less cost. Nonlinear and Anisotropic filter for removing speckle noise can be removed from US images. Proposed a modified Anisotropic filter which reduces speckle noises.
In the past two decades, the technique of image processing has made its way into every aspect of today’s tech-savvy society. Its applications encompass a wide variety of specialized disciplines including medical imaging, machine vision, remote sensing and astronomy. Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms. Image restoration can be described as an important part of image processing technique. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing. The method used to carry out the project was MATLAB software. Mathematical algorithms were programmed and tested for the result to find the necessary output. In this project mathematical analysis was the basic core. Generally the spatial and frequency domain methods were both important and applicable in different technologies. This project has tried to show the comparison between spatial and frequency domain approaches and their advantages and disadvantages. This project also suggested that more research have to be done in many other image processing applications to show the importance of those methods.
Digital image processing short quesstion answersAteeq Zada
This document discusses several techniques for 2D spatial image filtering and background subtraction in digital image processing. It covers linear filtering, Gaussian filtering, frame differencing, running averages, and mixtures of Gaussian models. The key techniques are linear filtering using a kernel or mask, Gaussian filtering to smooth images, and running averages or mixtures of Gaussians to model the background pixels over time while adapting to changes in illumination, motion, or scene geometry.
This document provides an overview of digital image processing and is divided into multiple parts. Part I discusses digital image fundamentals, image transforms, image enhancement, image restoration, image compression, and image segmentation. It introduces key concepts such as digital image systems, sampling and quantization, pixel relationships, and image transforms in both the spatial and frequency domains. Image processing techniques like filtering, histogram processing, and frequency domain filtering are also summarized.
image denoising technique using disctere wavelet transformalishapb
This document discusses image denoising techniques using discrete wavelet transforms. It begins with an introduction and lists the objectives, goals, and types of noise that affect images. It then describes several denoising techniques including spatial filtering methods like mean, wiener and median filters as well as frequency domain filtering and wavelet domain filtering. The document provides block diagrams of the wavelet denoising process and evaluates performance of various denoising algorithms using metrics like PSNR and SSIM. It was implemented in MATLAB and concluded that wavelet thresholding provides significant improvement in image quality while preserving useful information.
Reduced Ordering Based Approach to Impulsive Noise Suppression in Color ImagesIDES Editor
In this paper a novel filtering design intended for
the impulsive noise removal in color images is presented.
The described scheme utilizes the rank weighted cumulated
distances between the pixels belonging to the local filtering
window. The impulse detection scheme is based on the
difference between the aggregated weighted distances assigned
to the central pixel of the window and the minimum value,
which corresponds to the rank weighted vector median. If the
difference exceeds an adaptively determined threshold value,
then the processed pixel is replaced by the mean of the
neighboring pixels, which were found to be not corrupted,
otherwise it is retained. The important feature of the described
filtering framework is its ability to effectively suppress
impulsive noise, while preserving fine image details. The
comparison with the state-of-the-art denoising schemes
revealed that the proposed filter yields better restoration
results in terms of objective restoration quality measures.
This document discusses a technique for removing impulse noise from digital images using image fusion. It first filters a noisy input image using five different smoothing filters: median filter, vector median filter (VMF), basic vector directional filter (BVDF), switched median filter (SMF), and modified switched median filter (MSMF). The filtered images are then fused to obtain a single denoised output image with better quality than the individually filtered images. Edge detection is performed on the fused image using Canny filter to evaluate the noise cancellation performance from a human perception perspective. Experimental results show the proposed fusion technique produces better results compared to filtering with a single algorithm.
Speckle noise reduction from medical ultrasound images using wavelet threshIAEME Publication
This document proposes a method for reducing speckle noise from medical ultrasound images using wavelet thresholding and anisotropic diffusion. It first takes the logarithm of noisy images to convert speckle noise from multiplicative to additive. It then applies median filtering, followed by wavelet denoising using soft thresholding of detail coefficients. Finally, it uses SRAD filtering to enhance contrast while retaining edges. The proposed method is tested on three images and is found to improve SNR and PSNR compared to other filters based on statistical measurements.
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.
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.
This document discusses noise addition and filtering in images. It begins by introducing different types of digital images like binary, grayscale, and color images. It then discusses various sources of image noise like sensor heat, ISO settings, and memory failures. The main types of noise covered are salt and pepper noise, Gaussian noise, speckle noise, and uniform noise. Linear and non-linear filtering techniques are described for removing each noise type, including median filtering, Wiener filtering, and mean/Gaussian filtering. Performance of filters is evaluated using measures like mean squared error and peak signal-to-noise ratio. Matlab is mentioned for implementing noise addition and filtering.
This document is a project report on noise reduction in images using filters. It was submitted by 4 students - Priya M, Dondla Leela Vasundhara, Inderpreet Kaur, and Nisha Mathew - to the Department of Computer Science at Mount Carmel College in Bengaluru, India. The report discusses image processing techniques including different types of noise, noise reduction methods, and the use of filters to reduce noise in digital images.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document presents an approach for image deblurring based on sparse representation and a regularized filter. The approach involves splitting the blurred input image into patches, estimating sparse coefficients for each patch, learning dictionaries from the coefficients, and merging the patches. The merged patches are subtracted from the blurred image to obtain the deblur kernel. Wiener deconvolution with the kernel is then applied and followed by a regularized filter to recover the original image without blurring. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM showed it performed better than existing methods, recovering images with more details and contrast.
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.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
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.
This document presents a methodology for motion blur image restoration using an alternating direction balanced regularization filter. It begins with an introduction discussing image restoration and types of image degradation like blur and noise. It then discusses a literature review of existing techniques for motion blur parameter estimation and image restoration. The proposed methodology is described as estimating the motion blur angle and length using Gabor filters and radial basis functions, then restoring the image using an alternating direction balanced regularization filter. Experimental results on various standard test images are provided, comparing the proposed method to existing techniques based on metrics like PSNR and MSE. The conclusions discuss that the proposed method provides improved restoration quality over existing methods.
Yogesh Kumar presented on the topic of image restoration. The presentation discussed how image restoration aims to restore degraded images by applying the inverse of the known degradation process. It outlined key techniques for image restoration including inverse filtering, Wiener filtering, and non-linear filtering. The presentation also explained noise models, degradation models, and methods for estimating the degradation function - which is important for restoration. The goal of image restoration is to recover an approximation of the original image from a degraded version.
Halftoning of image is a way of compressing both RGB and grayscale image where instead of continuous levels or tone of pixels, only two discrete levels of pixels are considered. Actually a halftone image resembles a binary image in context of bits of pixels but the size and shape of pixels are modified to make it better in visualization. In this paper, we used two dimensional filtering techniques and discrete wavelet transform (DWT) with thresholding to recover an RGB image from its halftoned version. We compared the original and recovered image based on six largest eigen values, the SNR in dB and cross-correlation co-efficient of Red, Green and Blue components. The algorithm we used here shows 94% or above similarity between original and recovered image. This paper is actually the extended version of the previous paper of grayscale image.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
IRJET- A Novel Hybrid Image Denoising Technique based on Trilateral Filtering...IRJET Journal
The document proposes a novel hybrid image denoising technique based on trilateral filtering and Gaussian conditional random field modeling. It combines trilateral filtering, which is an edge-preserving Gaussian filter, with Gaussian conditional random fields to deal with different noise levels in images. The technique involves first applying trilateral filtering to smooth the image, then using Gaussian conditional random fields on the smoothed image. Experimental results on test images show the proposed technique achieves better denoising performance than traditional trilateral filtering alone, as measured by higher peak signal-to-noise ratios and lower mean squared errors.
This document discusses image restoration techniques for images degraded by space-variant blurs. It describes running sinusoidal transforms as a method for space-variant image restoration. Running transforms involve applying a short-time orthogonal transform within a moving window, allowing approximately stationary processing. This addresses limitations of methods that assume space-invariance or require coordinate transformations. The chapter presents running discrete sinusoidal transforms as a way to perform the space-variant restoration by modifying orthogonal transform coefficients within the window to estimate pixel values.
Speckle Noise Reduction in Ultrasound Images using Adaptive and Anisotropic D...Md. Shohel Rana
US Imaging Technique less cost. Nonlinear and Anisotropic filter for removing speckle noise can be removed from US images. Proposed a modified Anisotropic filter which reduces speckle noises.
In the past two decades, the technique of image processing has made its way into every aspect of today’s tech-savvy society. Its applications encompass a wide variety of specialized disciplines including medical imaging, machine vision, remote sensing and astronomy. Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms. Image restoration can be described as an important part of image processing technique. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing. The method used to carry out the project was MATLAB software. Mathematical algorithms were programmed and tested for the result to find the necessary output. In this project mathematical analysis was the basic core. Generally the spatial and frequency domain methods were both important and applicable in different technologies. This project has tried to show the comparison between spatial and frequency domain approaches and their advantages and disadvantages. This project also suggested that more research have to be done in many other image processing applications to show the importance of those methods.
Digital image processing short quesstion answersAteeq Zada
This document discusses several techniques for 2D spatial image filtering and background subtraction in digital image processing. It covers linear filtering, Gaussian filtering, frame differencing, running averages, and mixtures of Gaussian models. The key techniques are linear filtering using a kernel or mask, Gaussian filtering to smooth images, and running averages or mixtures of Gaussians to model the background pixels over time while adapting to changes in illumination, motion, or scene geometry.
This document provides an overview of digital image processing and is divided into multiple parts. Part I discusses digital image fundamentals, image transforms, image enhancement, image restoration, image compression, and image segmentation. It introduces key concepts such as digital image systems, sampling and quantization, pixel relationships, and image transforms in both the spatial and frequency domains. Image processing techniques like filtering, histogram processing, and frequency domain filtering are also summarized.
image denoising technique using disctere wavelet transformalishapb
This document discusses image denoising techniques using discrete wavelet transforms. It begins with an introduction and lists the objectives, goals, and types of noise that affect images. It then describes several denoising techniques including spatial filtering methods like mean, wiener and median filters as well as frequency domain filtering and wavelet domain filtering. The document provides block diagrams of the wavelet denoising process and evaluates performance of various denoising algorithms using metrics like PSNR and SSIM. It was implemented in MATLAB and concluded that wavelet thresholding provides significant improvement in image quality while preserving useful information.
Reduced Ordering Based Approach to Impulsive Noise Suppression in Color ImagesIDES Editor
In this paper a novel filtering design intended for
the impulsive noise removal in color images is presented.
The described scheme utilizes the rank weighted cumulated
distances between the pixels belonging to the local filtering
window. The impulse detection scheme is based on the
difference between the aggregated weighted distances assigned
to the central pixel of the window and the minimum value,
which corresponds to the rank weighted vector median. If the
difference exceeds an adaptively determined threshold value,
then the processed pixel is replaced by the mean of the
neighboring pixels, which were found to be not corrupted,
otherwise it is retained. The important feature of the described
filtering framework is its ability to effectively suppress
impulsive noise, while preserving fine image details. The
comparison with the state-of-the-art denoising schemes
revealed that the proposed filter yields better restoration
results in terms of objective restoration quality measures.
This document discusses a technique for removing impulse noise from digital images using image fusion. It first filters a noisy input image using five different smoothing filters: median filter, vector median filter (VMF), basic vector directional filter (BVDF), switched median filter (SMF), and modified switched median filter (MSMF). The filtered images are then fused to obtain a single denoised output image with better quality than the individually filtered images. Edge detection is performed on the fused image using Canny filter to evaluate the noise cancellation performance from a human perception perspective. Experimental results show the proposed fusion technique produces better results compared to filtering with a single algorithm.
Speckle noise reduction from medical ultrasound images using wavelet threshIAEME Publication
This document proposes a method for reducing speckle noise from medical ultrasound images using wavelet thresholding and anisotropic diffusion. It first takes the logarithm of noisy images to convert speckle noise from multiplicative to additive. It then applies median filtering, followed by wavelet denoising using soft thresholding of detail coefficients. Finally, it uses SRAD filtering to enhance contrast while retaining edges. The proposed method is tested on three images and is found to improve SNR and PSNR compared to other filters based on statistical measurements.
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.
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.
This document discusses noise addition and filtering in images. It begins by introducing different types of digital images like binary, grayscale, and color images. It then discusses various sources of image noise like sensor heat, ISO settings, and memory failures. The main types of noise covered are salt and pepper noise, Gaussian noise, speckle noise, and uniform noise. Linear and non-linear filtering techniques are described for removing each noise type, including median filtering, Wiener filtering, and mean/Gaussian filtering. Performance of filters is evaluated using measures like mean squared error and peak signal-to-noise ratio. Matlab is mentioned for implementing noise addition and filtering.
This document is a project report on noise reduction in images using filters. It was submitted by 4 students - Priya M, Dondla Leela Vasundhara, Inderpreet Kaur, and Nisha Mathew - to the Department of Computer Science at Mount Carmel College in Bengaluru, India. The report discusses image processing techniques including different types of noise, noise reduction methods, and the use of filters to reduce noise in digital images.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document presents an approach for image deblurring based on sparse representation and a regularized filter. The approach involves splitting the blurred input image into patches, estimating sparse coefficients for each patch, learning dictionaries from the coefficients, and merging the patches. The merged patches are subtracted from the blurred image to obtain the deblur kernel. Wiener deconvolution with the kernel is then applied and followed by a regularized filter to recover the original image without blurring. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM showed it performed better than existing methods, recovering images with more details and contrast.
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.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
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.
This document presents a methodology for motion blur image restoration using an alternating direction balanced regularization filter. It begins with an introduction discussing image restoration and types of image degradation like blur and noise. It then discusses a literature review of existing techniques for motion blur parameter estimation and image restoration. The proposed methodology is described as estimating the motion blur angle and length using Gabor filters and radial basis functions, then restoring the image using an alternating direction balanced regularization filter. Experimental results on various standard test images are provided, comparing the proposed method to existing techniques based on metrics like PSNR and MSE. The conclusions discuss that the proposed method provides improved restoration quality over existing methods.
Yogesh Kumar presented on the topic of image restoration. The presentation discussed how image restoration aims to restore degraded images by applying the inverse of the known degradation process. It outlined key techniques for image restoration including inverse filtering, Wiener filtering, and non-linear filtering. The presentation also explained noise models, degradation models, and methods for estimating the degradation function - which is important for restoration. The goal of image restoration is to recover an approximation of the original image from a degraded version.
Halftoning of image is a way of compressing both RGB and grayscale image where instead of continuous levels or tone of pixels, only two discrete levels of pixels are considered. Actually a halftone image resembles a binary image in context of bits of pixels but the size and shape of pixels are modified to make it better in visualization. In this paper, we used two dimensional filtering techniques and discrete wavelet transform (DWT) with thresholding to recover an RGB image from its halftoned version. We compared the original and recovered image based on six largest eigen values, the SNR in dB and cross-correlation co-efficient of Red, Green and Blue components. The algorithm we used here shows 94% or above similarity between original and recovered image. This paper is actually the extended version of the previous paper of grayscale image.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
IRJET- A Novel Hybrid Image Denoising Technique based on Trilateral Filtering...IRJET Journal
The document proposes a novel hybrid image denoising technique based on trilateral filtering and Gaussian conditional random field modeling. It combines trilateral filtering, which is an edge-preserving Gaussian filter, with Gaussian conditional random fields to deal with different noise levels in images. The technique involves first applying trilateral filtering to smooth the image, then using Gaussian conditional random fields on the smoothed image. Experimental results on test images show the proposed technique achieves better denoising performance than traditional trilateral filtering alone, as measured by higher peak signal-to-noise ratios and lower mean squared errors.
The document proposes a method for image enhancement through noise suppression using a Nonlinear Parameterized Adaptive Recursive (PAR) model in the spatial domain. The PAR model uses an intentional median filter that performs filtering only on noisy pixels, adaptively varying the window size and number of iterations. Experimental results on images corrupted with salt and pepper noise show the PAR model achieves better noise suppression than traditional, recursive, and adaptive median filters as measured by higher peak signal-to-noise ratios and shorter computational times. The PAR model is thus useful for interactive image processing by providing a family of possible denoised images.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
A new methodology for sp noise removal in digital image processing ijfcstjournal
The paper purposes the removal of noise in digital gray scale images that often observed in scanned
documents. Generally, Data i.e. picture, text can be contaminated by an additive noise during the process
of scanning. This methodology prevents this type of noise known as Salt and Pepper noise (SP Noise) which
causes white and black spots on the original image. We are designing a new algorithm for removal of these
white and black spots after the knowledge of Median Filter, Adaptive Filter and the new proposed
algorithm will definitely protect the image from noise and distortion. Firstly, Adaptive Histogram
Equalization is done on the original image. Secondly apply Adaptive contrast Enhancement Technique on
the resultant image. After Contrast Enhancement we apply filters Such as Homomorphic filtering. These
filters are applied sequentially on distorted images for removing the image.
Image restoration aims to recover an original image that has been degraded. Restoration filters are used to estimate the clean image by reversing blurring or other degradation processes. Both spatial and frequency domain filters can be used, with spatial filters suitable for noise removal and frequency filters used for deblurring. A standard image degradation model involves convolution of the original image with a degradation function plus additive noise. The goal of restoration is to estimate the original image given the degraded image and knowledge of the degradation characteristics and noise model. Common noise models include Gaussian, Rayleigh, gamma, and salt and pepper noise. Spatial filters like the median and adaptive median filter are often used to remove noise.
Performance Comparison of Various Filters and Wavelet Transform for Image De-...IOSR Journals
This document compares different filtering and wavelet transform approaches for image de-noising. It adds three types of noise (Gaussian, salt and pepper, speckle) to an image and uses median, Wiener, Gaussian, average filters and wavelet transform to remove the noise. It evaluates the performance of each approach using peak signal-to-noise ratio and root mean square error. The results show that wavelet transform performs best for removing Gaussian and speckle noise, while median filtering works best for salt and pepper noise removal. Overall, wavelet transform is concluded to be very effective for de-noising all types of noise.
The document proposes modified algorithms based on rank-order statistics and switching schemas for restoring digital images corrupted by salt-and-pepper noise and random valued impulse noise. It introduces the Modified Progressive Switching Median Filter (MPSM) which uses a noise detection step followed by a filtration procedure. For salt-and-pepper noise, MPSM achieves 1-2 dB higher PSNR than other filters. For random valued impulse noise, MPSM provides 0.5 dB higher PSNR than other algorithms.
IRJET- A Review on Various Restoration Techniques in Digital Image ProcessingIRJET Journal
The document reviews various image restoration techniques used for removing noise and blurring from digital images. It discusses techniques like median filtering, Wiener filtering, and Lucy Richardson algorithms. It provides an overview of each technique, including their advantages and limitations. The document also reviews several research papers that propose modifications to existing techniques or new methods for tasks like salt-and-pepper noise removal. The reviewed papers found that their proposed methods improved restoration quality over other techniques, achieving higher PSNR values and producing images that looked visually sharper and more distinct.
This document provides an overview of digital image processing, specifically focusing on image restoration and segmentation. It discusses common image degradation models and noise models, including Gaussian, Rayleigh, gamma, exponential, uniform, and impulse noise. Spatial filtering techniques for image restoration when only noise is present are described, including mean, median, max, min, and midpoint filters. Image segmentation techniques include detection of discontinuities, edge linking, boundary detection, thresholding, and region-oriented segmentation using motion. The document is intended to support a course on digital image processing.
Final presentation(image enhancement system)Hammaad Khan
Title: Image Enhancement System.
Our project was in MATLAB simulation..
All the work we have done on images... This was our presentation done on our finel viva in International confrence 2013.. thanks honorable Sir Salman AWKUM.. This man helped us much..
This document provides an overview of digital image processing techniques for image restoration. It defines image restoration as improving a degraded image using prior knowledge of the degradation process. The goal is to recover the original image by applying an inverse process to the degradation function. Common degradation sources are discussed, along with noise models like Gaussian, salt and pepper, and periodic noise. Spatial and frequency domain filtering techniques are presented for restoration, such as mean, median and inverse filters. The maximum mean square error or Wiener filter is also introduced as a way to minimize restoration error.
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.
Survey on Noise Removal in Digital ImagesIOSR Journals
This document summarizes several algorithms for removing noise from digital images. It focuses on three common types of noise (impulse, speckle, and Gaussian noise) and three types of images (sensor, medical, and grayscale). For each noise/image combination, several filtering algorithms are described and compared based on their ability to remove noise while preserving important image details. The document concludes that the best algorithm depends on the specific noise and image type, and suggests the need for further research to identify optimal noise removal methods.
Digital image processing techniques can be used to enhance images by modifying pixel values using filters. Filters are classified as either spatial or frequency domain filters, with non-linear filters being more effective at edge detection than linear filters. The median filter is a common non-linear filter that replaces pixel values with the median of neighboring pixels to reduce salt-and-pepper noise. Image restoration techniques aim to reduce noise and recover lost resolution, such as by using deconvolution in the frequency domain to undo the effects of blurring.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
This document describes a new method for enhancing grayscale images corrupted by salt and pepper noise. The proposed improved mean filter method is compared to other filters like standard median filter, decision based median filter, and modified decision based median filter. The proposed method achieves better performance in terms of peak signal to noise ratio, image enhancement factor, and visual perception, especially for low density impulse noise. It works by replacing noisy pixels with the trimmed mean value of neighboring pixels, improving on previous methods. Experimental results on test grayscale images demonstrate the effectiveness of the proposed filter.
Image Restoration Using Particle Filters By Improving The Scale Of Texture Wi...CSCJournals
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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.
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Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
1. PARALLEL PROCESSING
FOR DIGITAL IMAGE
ENHANCEMENT
Nora Youssef Fahmy
B.Sc. in Computer and Information Sciences,
Teaching assistant at Computer Science Department
Faculty of Computer and Information Sciences
Ain Shams University
Supervised By
Prof. Dr. El-Sayed M. El-Horbaty
Dr. Abeer M. Mahmoud
Cairo 2015
2. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
3. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
5. GRAYSCALE IMAGE
Monochrome / One color image
No color info
Represent the brightness of the image
8 bits/pixel data 256 different brightness
level
6. MEDICAL IMAGE
Used to create images of the human body
Seeks to
Reveal internal structures hidden by the skin and bones
Diagnose and treat disease
PET CT MRI
7. DEGRADATION MODEL
Noises
Domains
Filters
𝒈(𝒙, 𝒚)
𝒇^
(𝒙, 𝒚)
Noise
𝜼(𝒙, 𝒚)
+
DEGRADATION RESTORATION
𝒇(𝒙,𝒚)
Degradation
function
H
Restoration
Filter(s)
9. SALT & PEPPER NOISE
Additive
𝑝 𝑧 =
𝑃𝑎 𝑓𝑜𝑟 𝑧 = 𝑎
𝑃𝑏 𝑓𝑜𝑟 𝑧 = 𝑏
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Due To:
Quick transients
Data transmission errors
10. IMAGE ENHANCEMENT &
RESTORATION
Attempts to recover an image that has been distorted
by using a priori knowledge of the degradation
process.
𝒈(𝒙, 𝒚)
𝒇^
(𝒙, 𝒚)
Noise
𝜼(𝒙, 𝒚)
+
DEGRADATION RESTORATION
𝒇(𝒙,𝒚)
Degradation
function
H
Restoration
Filter(s)
12. SPATIAL DOMAIN
𝑔(𝑥, 𝑦) = 𝑇[𝑓(𝑥, 𝑦)]
Direct manipulation of pixels in an
image
Covers the neighborhood operations
(Convolution)
Filters in this domain are time
consuming
14. TRANSFORM DOMAIN
(FREQUENCY DOMAIN)
𝐹(𝑢, 𝑣) = 𝑅[𝑓(𝑥, 𝑦)]
𝑔(𝑥, 𝑦) = 𝑅−1 𝑇[𝐹(𝑢, 𝑣)]
A representation of an image as a sum of complex
exponentials of varying magnitudes, frequencies, and
phases
15. FREQUENCY FILTERING
Filters in spatial domain have their corresponding form in
frequency domain
Convolution in the spatial domain corresponds to multiplication in
the frequency domain, and vice versa
Wiener filter
Gaussian removal
De-blurring
23. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
24. PROBLEM DEFINITION
Images are corrupted by multi-noise type (i.e. Gaussian, salt &
pepper) at the same time.
There is an open demand for multi-noise removal filters.
25. PROBLEM DEFINITION
Medical images have very large size
Processing these forms take so much time to process
sequentially
We have to parallelize the traditional sequential
algorithms for the sake of time performance
improvement.
26. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
27. OBJECTIVES
1. Study the literature work and implement Gaussian de-noising
experiment as a case study.
28. OBJECTIVES
2. Design & Develop sequential hybrid de- noising filter for multi-
noise removal and compare between it and the simple filters in terms
of peak signal to noise ratio (PSNR).
29. OBJECTIVES
3. Design & Develop a parallel hybrid de-noising filter then
compare it relative to the sequential hybrid above in terms of
time.
30. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
31. RELATED WORK
Single Noise Removal
Multi-Noise Removal
Parallel Image Processing
35. Year Authors Distortion Hybrid Input
Quality
Criteria
2014 Seema and
Meenakshi
G.
Speckle + Gaussian Median Filter based on DWT
via soft thresholding +
Mean absolute difference
Standard gray scale
images
PSNR
2013 Ankita D.
and et.al
Camera & object
motion blurs +
Gaussian +
Salt & pepper
Wiener + Median Gradient images PSNR
MSE
2013 Versha R.
and
Priyanka K.
Gaussian +
impulsive +
Speckle +
Possion
Curvelet transform +
Unsharp Mask filter +
Median filter
One gray & One
colored images
PSNR
2012 J U. and G
R.
Gaussian + Impulsive Haar wavelet filter +
soft thresholding technique +
center weighted median
10 DICOM images PSNR
MAE
UQI
ET
49. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure hybridFilter (noisyBuf)
adapOnly = apply adaptive
median filter
adapMedian(noisyBuf, 7 pix)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure degradeImage(original)
I1 = Add circular blur to original
I2 = Add Gaussian noise to I1
I3 = Add Salt & Pepper noise to I3
return I3 as Noisy buffer
end procedure
1
50. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure hybridFilter (noisyBuf)
adapOnly = apply adaptive
median filter
adapMedian(noisyBuf, 7 pix)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure adapMedian(g, Smax)
f = g; %initial setup
alreadyProcessed = false(size(g));
for all window size k = start from 3 to Smax do:
get Min, max and median Values of window and
store it in zmin, zmax and zmed;
determine if we will use level b processing in
processByLvlB;
zB = (g > zmin) & (zmax > g);
outputZxy = processByLvlB & zB;
outputZmed = processByLvlB & ~zB;
f(outputZxy) = g(outputZxy);
f(outputZmed) = zmed(outputZmed);
alreadyProcessed = alreadyProcessed or
processUsingLevelB;
if all alreadyProcessed array has been done
break the loop
end if
end for
fill the remaining values in alreadyProcessed if
exist by the zmed
end procedure
1
2
51. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure hybridFilter (noisyBuf)
adapOnly = apply adaptive
median filter
adapMedian(noisyBuf, 7 pix)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure wiener(adapOnly, original)
F = create power spectrum of the adapOnly
sigma_u = calculate noise sigma in F
output = wiener deconvolution for F given the
original image and sigma_u
end procedure
1
2
3
52. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure hybridFilter (noisyBuf)
adapOnly = apply adaptive
median filter
adapMedian(noisyBuf, 7 pix)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure contrast(image)
stretch image’s pixels values to fit in range 0 -
255
end procedure
1
2
3
4
53. ENVIRONMENT SETTINGS
Input:
Mode: Gray scale – no color info
Dimensions:
512 X 512 (0.5 MB)
128 X 128 (17 KB)
Resolution: 300px
Distortion
Gaussian
Salt & Pepper
Circular Blur
Machine Specs:
Processor: Intel core i5
RAM: 4 GB
OS: Windows 7 home edition, 64 bit
Tool:
54. RESULTS
Average PSNR for Adaptive, Wiener and Serial Hybrid
Image a b c d e f g h I J k l AVG
Adaptive 10.04 14.15 14.2 12.06 13.3 13.7 13.43 9.5 13.29 17.12 6.88 12.45 12.6
Wiener 11.63 23.37 18.63 14.37 16.46 8.79 12.39 11.6 15.06 22.53 11.92 16.73 15.3
Hybrid 13.36 29.7 26.7 22.75 19.32 18.34 17.66 18.84 18.97 26.04 24.69 20.64 19.8
55. 0
5
10
15
20
25
30
35
a b c d e f g h i j k l
PSNR
IMAGE
PSNR chart for full size images for proposed hybrid approach, adaptive
& wiener
Hybrid
Wiener
Adaptive
56. 0
5
10
15
20
25
30
35
a b c d e f g h i j k l
PSNR
IMAGE
Thumbnails VS. full Sizes of PSNR values
Thumbnails
Full Sizes
57. 0
10
20
30
40
50
60
a b c d e f g h i j k l
TIME(MINS)
IMAGE
Thumbnails VS. full Sizes time plot in minutes
Thumbnails
Full Size
58. PARALLEL HYBRID
ALGORITHM
Adaptive Median takes
too much time
Spatial domain
Window size increases
Fourier
Transform
Wiener
Inverse
Fourier
Transform
Adaptive
Median
Add Gaussian
Add Salt & Pepper
Circular Blur
Image
Noisy
Image
Power
Spectrum
Restored
Image
Spatial
Domain
Fourier
Transform
Domain
62. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure parallelHybridFilter
(noisyBuf)
adapOnly = apply adaptive
median filter
parAdapMedian(noisyBuf, 11
pix, workersNo)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure degradeImage(original)
I1 = Add circular blur to original
I2 = Add Gaussian noise to I1
I3 = Add Salt & Pepper noise to I3
return I3 as Noisy buffer
end procedure
1
63. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure parallelHybridFilter
(noisyBuf)
adapOnly = apply adaptive
median filter
parAdapMedian(noisyBuf, 11
pix, workersNo)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure parAdapMedian(g, Smax, workersNo)
for each worker in workersNo do in Parallel
apply adaptive median filter convolution
procedure on g
end for
end procedure
1
2
64. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure parallelHybridFilter
(noisyBuf)
adapOnly = apply adaptive
median filter
parAdapMedian(noisyBuf, 11
pix, workersNo)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure wiener(adapOnly, original)
F = create power spectrum of the adapOnly
sigma_u = calculate noise sigma in F
output = wiener deconvolution for F given the
original image and sigma_u
end procedure
1
2
3
65. PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure parallelHybridFilter
(noisyBuf)
adapOnly = apply adaptive
median filter
parAdapMedian(noisyBuf, 11
pix, workersNo)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure contrast(image)
stretch image’s pixels values to fit in range 0 -
255
end procedure
1
2
3
4
66. ENVIRONMENT SETTINGS
Input:
Mode: Gray scale – no color info - 500px
Dimensions:
1900 x 2368 (277 KB)
3800 x 4736 (715 KB)
15200 x 18944 (2.5 MB)
Distortion:
Gaussian
Salt & Pepper
Circular Blur
Machine Specs:
Processor: Intel core i5
RAM: 6 GB
OS: Windows 7 enterprise edition, 64 bit
Tool
67. RESULTS
Time in seconds for the 3 image sizes each of which
divided into 2,4 an 8 partitions
Workers rang 2 - 12
Image Dimensions Serial Workers 2 4 6 8 10 12
1900 X 2368 79.5
2-Partitions 10.41 10.87 10.89 10.37 10.96 11.11
4- Partitions 12.1 11.99 12.22 12.29 12.49 12.23
8- Partitions 16.22 15.84 15.87 16.62 15.64 16.2
3800 X 4736 272.42
2- Partitions 44.67 41.59 36.14 35.12 42.86 49.28
4- Partitions 41.37 40.76 40.62 41.06 41.35 42.18
8- Partitions 54.88 54.65 56.63 56.85 54.69 55.21
7600 X 9472 1742.5
2- Partitions 640.34 863.87 856.24 652.75 914.24 690.8
4- Partitions 768 470.08 480.03 532.75 490.92 703.38
8- Partitions 847.9 538.55 769.91 505.67 656.4 611.59
68. 0
100
200
300
400
500
600
700
800
2 4 6 8 10 12
TIME(SECONDS)
WORKER
Average time consumed in seconds for serial and parallel 2,4 and 8
partition input
Serial
2-Partition
4-Partition
8-Partition
71. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
72. CONCLUSIONS
Simple Gaussian filters comparison showed for the average run 3, 5,
7 and 9 pix that
Harmonic filter gave the max. PSNR
Geometric filter gave the min. Time
74. CONCLUSIONS
Parallel hybrid filter gave a speed up
3x for 2-partition
4x for 4-partition
3.5x for 8-partition
75. FUTURE WORK
Apply the proposed methods medical imaging modalities like PET
(Colored).
Try to corrupt the image with higher noise probabilities.
Change the noise type to but take care that it suits the selected
adaptive median and wiener filters.
IMAGE PROCESSING
76. FUTURE WORK
Try Different parallel model
GPU
CPU + GPU (Hybrid Parallel).
Compare time performance with the existing methods.
Integrate with cloud computing.
PARALLEL PROCESSING
77. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
78. PUBLICATIONS
Nora Youssef, Abeer M.Mahmoud and EL-
Sayed M.EL-Horbaty “Gaussian De-
Noising Techniques in Spatial Domain for
Gray Scale Medical Images”, Int. J. of
Information Technologies and Knowledge
(ITK), Vol. 8, No.3, pp.90-100, Bulgaria,
Jun. 2014.
79. PUBLICATIONS
Nora Youssef, Abeer M.Mahmoud and
EL-Sayed M.EL-Horbaty “A Hybrid De-
Noising Technique for Multi-noise
Removal on Gray Scale Medical
Images”, Int. J. of Tomography and
Simulation (IJTS) IF 0.75, Vol. 28, No.2, pp
106-116, India, Mar. 2015.
81. PUBLICATIONS
Nora Youssef, Abeer M.Mahmoud and
EL-Sayed M.EL-Horbaty “A Parallel Hybrid
Technique for Multi-Noise Removal from
Grayscale Medical Images” Int. J Real
Time Image Processing, Springer, Berlin,
May 2015 (Submitted).
82. AGENDA
Introduction
Problem Definition
Objectives
Related Work
Methodologies
Conclusions & Future Work
Publications
References
83. REFERENCES
Ravi M. Rai and Urooz J. “Analysis Techniques For Eliminating Noise In Medical
Images Using Bivariate Shrinkage Method” Int. J. of Advanced Research in
Computer Engineering & Technology vol. 2, no. 10, pp. 2737 to 2740, 2013.
Ankita D. and Archana S. “An Advanced Filter for Image Enhancement and
Restoration” J. Open Journal of Advanced Engineering Techniques OJAET vol.1,
no. 1, pp. 7-10, 2013.
Salem Al-amri, N. V. Kalyankar and S. D. Khamitkar “A Comparative Study of
Removal Noise from Remote Sensing Image” Int. J. of Computer Science Issues,
vol. 7, no. 1, pp. 32 - 35, 2010.
Sanjay S., Neeraj S. and Shiru S. "Image Processing Tasks using Parallel
Computing in Multi core Architecture and its Applications in Medical Imaging"
Int. J. of Advanced Research in Computer and Communication Engineering
vol. 2, no. 4, pp. 1896 to 1899, 2013
84. REFERENCES
Rajeshwari S., Sharmila T. Sree “Efficient quality analysis of MRI image using
preprocessing techniques,” Proceedings of 2013 IEEE Conference on Information
and Communication Technologies (ICT), pp.: 391-396, 2013.
Rafael C. Gonzalez and Richard E. Woods “Digital Image Processing” Person
Education,3rd Edition, 2008.
Nikola R. and Milan T. “Improved Adaptive Median Filter for Denoising Ultrasound
Images” Conf. Advances in Computer Science pp. 196 - 174, 2012
P.Deepa and M.Suganthi “Performance Evaluation of Various Denoising Filters for
Medical Image”, IJCSIT, Vol. 5, No.3, pp. 4205-4209 , 2014
85. REFERENCES
B.Mohd. Jabarullah, Sandeep Saxena and Dr.C. Nelson Kennedy Badu“Survey
on Noise Removal in Digital Images” Vo. 6, No. 4, pp.45 -51 2012
Gajanand G.“Algorithm for Image Processing Using Improved Median Filter and
Comparison of Mean, Median and Improved Median Filter” IJSCE, vol. 1, no.5
pp. 304 – 311, 2011.
K.Selvanayaki, Dr. M. Karnan “CAD System for Automatic Detection of Brain
Tumor through Magnetic Resonance Image-A Review” Int. J. of Engineering
Science and Technology vol. 2, no.10, pp. 5890-5901, 2010.
Monika P. and Sukhdev S. “Comparative Analysis of Image Denoising
Techniques” Int. J. of Computer Science & Engineering Technology (IJCSET) vol.
5 no. 02 pp. 160 - 167, 2014
89. original = imread(imPath);
original = im2double(original);
noisyBuf = degradImage(original);
procedure hybrid (noisyBuf)
adapOnly = apply adaptive median filter (noisyBuf, 7);
hybrid = apply wiener filter (adapOnly, original);
hybrid = apply contrast(hybrid);
end procedure
procedure adapMedian(g, Smax)
f = g; %initial setup
alreadyProcessed = false(size(g));
for all window size k = start from 3 to Smax do:
zmin = get Min Value of window;
zmax = get Max Value of window;
zmed = get Median Value of window;
processByLvlB determine if we will use level b
processing;
zB = (g > zmin) & (zmax > g);
outputZxy = processByLvlB & zB;
outputZmed = processByLvlB & ~zB;
f(outputZxy) = g(outputZxy);
f(outputZmed) = zmed(outputZmed);
alreadyProcessed = alreadyProcessed or
processUsingLevelB;
if all alreadyProcessed array has been done
break the loop
end if
end for
fill the remaining values in alreadyProcessed if exist by
the zmed
end procedure
SERIAL CODE
90. PARALLEL CODE
original = imread(imPath);
original = im2double(original);
noisyBuf = degradImage(original);
procedure parallelHybrid (noisyBuf)
adapOnly = parallelAdapMedian(noisyBuf, 11, partition_i);
partition_i belongs to {2, 4, 8};
hybrid = apply Wiener function(adapOnly, original);
hybrid = apply contrast(hybrid);
end procedure
procedure parallelAdapMedian(g, Smax, parts)
[M, N] = size(g);
f = g; %initial setup
alreadyProcessed = false(size(g));
splittedImg = imgSpliter( f, M, N, parts );
for subimg = 1 into parts, numberOfWorkers_i do in parallel
numberOfWorkers_i belongs to {2, 4, 6 , 8, 10, 12}
myTemp = splittedImg(subimg);
t = alreadyProcessed;
for k = 3 to Smax do
zmin = get Min. value in subimg
zmax = get Max. value in subimg
zmed = get Median value in subimg
processByLvlB = determine if we will use level b
processing
zB = (g > zmin) & (zmax > g);
outputZxy = processByLvlB & zB;
outputZmed = processByLvlB & ~zB;
myTemp(outputZxy) = g(outputZxy);
myTemp(outputZmed) = zmed(outputZmed);
end for
splittedImg(subimg) = myTemp;
end parfor
end procedure
91. DATA SETS
idoimagaing
http://idoimaging.com/wiki/tiki-
index.php?page=Sample+Data
3DISC
http://www.3discimaging.com/our-products/medical-
solutions/firecr-medicalreaders/image-quality/
Computed Tomography Emphysema Database
http://image.diku.dk/emphysema_database/
Some of images are available on Google Images.
96. A PARALLEL HYBRID TECHNIQUE
FOR MULTI-NOISE REMOVAL
FROM GRAYSCALE MEDICAL
IMAGES
Submitted
Int. J. Real Time Image Processing
Springer
IF: 1.1
Germany
2015
Click Here ...
Editor's Notes
PET - Positron Emission Tomography
CT - Computerized Tomography
MRI - Magnetic Resonance Imaging
Ref: MNT Knowledge Center web link http://www.medicalnewstoday.com/articles/146309.php , Access date 9/1/2014
Green are commonly used for Gaussian removal
Red are commonly used for Sal & Pepper removal
Figure: Original image vs. Fourier magnitude representation
MSE Mean Square Error
SNR Signal to Noise Ratio
PSNR Peak Signal to Noise Ratio
Coarse-grained Few tasks but have large computations (Functional Decomposition)
Fine-grained Many tasks but have small computations (Data Decomposition)
MSE Mean Square Error
PSNR Peak Signal to Noise Ratio
CoC Correlation Coefficients
EPI Edge Preservation Index
MAE Mean Absolute Error
UQI Universal Quality Index
ET Execution Time (It was abbreviated by the paper’s Authors)
----
Ref to Book Chapter page 45 for full index
MSE Mean Square Error
PSNR Peak Signal to Noise Ratio
CoC Correlation Coefficients
EPI Edge Preservation Index
MAE Mean Absolute Error
UQI Universal Quality Index
ET Execution Time (It was abbreviated by the paper’s Authors)
----
Ref to Book Chapter page 45 for full index
Sample run of 3x3 pix filter size
A b
C d E from paper
Correctness
1. Initially we begin a 2D image matrix (I) with size NxN distorted by 3 types of noise, Blur (B), Gaussian (G) and Salt & Pepper (S&P).
2. The Selected adaptive median (am) filter will scan (I) pixel by pixel in a sequential manner for S&P removal.
3. (am) filter will terminate after a finite iterations based on (I) size NxN and subwindow size mxm.
4. The selected Wiener (w) filter will scan the image power spectrum (PS) for (B) and (G) removal and terminates on the end of the PS.
5. The whole filter will terminate after (w) filter termination as it is the last step in the hybrid filter.
Adaptive Median is the bottleneck
Because it runs on spatial domain and the filter on the worst case runs 3, 5, 7, 9, 11 px so that the filter size increases.
Adaptive Median is the bottleneck
Because it runs on spatial domain and the filter on the worst case runs 3, 5, 7, 9, 11 px so that the filter size increases.
Adaptive Median is the bottleneck
Because it runs on spatial domain and the filter on the worst case runs 3, 5, 7, 9, 11 px so that the filter size increases.
Adaptive Median is the bottleneck
Because it runs on spatial domain and the filter on the worst case runs 3, 5, 7, 9, 11 px so that the filter size increases.
Correctness
1. Initially we begin a 2D image matrix (I) with size NxN distorted by 3 types of noise, Blur (B), Gaussian (G) and Salt & Pepper (S&P).
2. Input image will be divided into k partitions.
3. The Selected adaptive median (am) filter remove the S&P noise from the k partitions in parallel.
4. (am) filter will terminate after a finite iterations based on I size NxN and subwindow size mxm.
5. The selected Wiener (w) filter will scan the image power spectrum (PS) for B and G removal and terminates on the end of the PS.
6. The whole filter will terminate scan the k partitions of the image and (w) filter termination.