Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
FAN search for image copy-move forgery-amalta 2014SondosFadl
The proposed Fan Search (FS) algorithm starts once a duplicated block is detected. Instead of exhaustive search for all blocks,the nearby blocks of the detected block are examined first in a spiral order.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
FAN search for image copy-move forgery-amalta 2014SondosFadl
The proposed Fan Search (FS) algorithm starts once a duplicated block is detected. Instead of exhaustive search for all blocks,the nearby blocks of the detected block are examined first in a spiral order.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
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.
3 ijaems nov-2015-6-development of an advanced technique for historical docum...INFOGAIN PUBLICATION
In this paper, technique used for historical document preservation is explored. In this paper a noise estimation technique is applied to know noise standard deviation. We first estimate or detect level of noise present in noisy images by selecting weak textured patches in image on the basis of gradient matrix and its statistical properties, then eliminate that noise through non local means(NLM) denoising technique that will use estimated noise level as filtering parameter for eliminating noise from the image. This technique is based on weighted average of the similar pixels in historical image. Non local means techniques removes noise from images without taking care of noise level ,it is mandatory to take care of noise level for best preserving Historical document images.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
An improved hdr image processing using fast global tone mappingeSAT Journals
Abstract People always try to reconstruct images that look like original scene. But it is not succeeded up to the expected level because the display devices cannot accommodate the actual range of illumination. The original scene is having high dynamic range (HDR) and the display devices are having low dynamic range (LDR) of values. Tone mapping can be used to display HDR image in an LDR device. In this paper we are trying to develop a better tone mapping method. A number of images with different exposure are fused to form an HDR image.. Here we used average method for this which is the simplest one. Then by using tone mapping the HDR image is converted into LDR. We tried Tone Reproduction Curve (TRC) based global tone mapping. TRC method results faster operation The global tone mapping operator algorithm is simple and does not introduce ghosting. The image will also free from blur and halo like artifacts. We took log average of the images to keep the pixel values within limit. Index Terms: Image fusion, High dynamic range images, , Tone mapping. Low dynamic range image, global operator.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
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.
3 ijaems nov-2015-6-development of an advanced technique for historical docum...INFOGAIN PUBLICATION
In this paper, technique used for historical document preservation is explored. In this paper a noise estimation technique is applied to know noise standard deviation. We first estimate or detect level of noise present in noisy images by selecting weak textured patches in image on the basis of gradient matrix and its statistical properties, then eliminate that noise through non local means(NLM) denoising technique that will use estimated noise level as filtering parameter for eliminating noise from the image. This technique is based on weighted average of the similar pixels in historical image. Non local means techniques removes noise from images without taking care of noise level ,it is mandatory to take care of noise level for best preserving Historical document images.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
An improved hdr image processing using fast global tone mappingeSAT Journals
Abstract People always try to reconstruct images that look like original scene. But it is not succeeded up to the expected level because the display devices cannot accommodate the actual range of illumination. The original scene is having high dynamic range (HDR) and the display devices are having low dynamic range (LDR) of values. Tone mapping can be used to display HDR image in an LDR device. In this paper we are trying to develop a better tone mapping method. A number of images with different exposure are fused to form an HDR image.. Here we used average method for this which is the simplest one. Then by using tone mapping the HDR image is converted into LDR. We tried Tone Reproduction Curve (TRC) based global tone mapping. TRC method results faster operation The global tone mapping operator algorithm is simple and does not introduce ghosting. The image will also free from blur and halo like artifacts. We took log average of the images to keep the pixel values within limit. Index Terms: Image fusion, High dynamic range images, , Tone mapping. Low dynamic range image, global operator.
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Performance Evaluation of Image Edge Detection Techniques CSCJournals
The success of an image recognition procedure is related to the quality of the edges marked. The
aim of this research is to investigate and evaluate edge detection techniques when applied to
noisy images at different scales. Sobel, Prewitt, and Canny edge detection algorithms are
evaluated using artificially generated images and comparison criteria: edge quality (EQ) and map
quality (MQ). The results demonstrated that the use of these criteria can be utilized as an aid for
further analysis and arbitration to find the best edge detector for a given image.
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion RatioCSCJournals
We intend to make a 3D model using a stereo pair of images by using a novel method of local matching in pixel domain for calculating horizontal disparities. We also find the occlusion ratio using the stereo pair followed by the use of The Edge Detection and Image SegmentatiON (EDISON) system, on one the images, which provides a complete toolbox for discontinuity preserving filtering, segmentation and edge detection. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. We then warp the segment disparities to the original image to get our final 3D viewing Model.
IMAGE ENHANCEMENT IN CASE OF UNEVEN ILLUMINATION USING VARIABLE THRESHOLDING ...ijsrd.com
Uneven illumination always affects the visual quality images which results in poor understanding about the content of the images. There is no accepted universal image enhancement algorithm or specific criteria which can fulfill user needs. The processed image may be very different with the original image in the visual effects, but it also may be similar to the original image [1]. It will be a developing tradition to integrate the advantage of various algorithms to practical application to image enhancements [2]. Zhang et al. [3] presents an adaptive image contrast enhancement method. The proposed method is based on a local gamma correction piloted by histogram analysis. In this paper , to avoid uneven Illuminance image is divided into different segments . It works locally to decrease contrast as if we perform enhancement techniques globally on portions which are already bright then this gives poor results. Enhancement techniques are applied only to those dark portions. We need accurate method that not only enhance the image but also preserve the information.
A methodology for visually lossless jpeg2000 compression of monochrome stereo...Kamal Spring
A methodology for visually lossless compression of monochrome stereoscopic 3D images is proposed.
Visibility thresholds are measured for quantization distortion in JPEG2000. These thresholds are found to be functions of not only spatial frequency, but also of wavelet coefficient variance, as well as the gray level in both the left and right images.
To avoid a daunting number of measurements during subjective experiments, a model for visibility thresholds is developed.
The left image and right image of a stereo pair are then compressed jointly using the visibility thresholds obtained from the proposed model to ensure that quantization errors in each image are imperceptible to both eyes.
This methodology is then demonstrated via a particular 3D stereoscopic display system with an associated viewing condition.
The resulting images are visually lossless when displayed individually as 2D images, and also when displayed in stereoscopic 3D mode.
Post-Segmentation Approach for Lossless Region of Interest Codingsipij
This paper presents a lossless region of interest coding technique that is suitable for interactive telemedicine over networks. The new encoding scheme allows a server to transmit only a part of a compressed image data progressively as a client requests it. This technique is different from region scalable coding in JPEG2000 since it does not define region of interest (ROI) when encoding occurs. In the proposed method, the image is fully encoded and stored in the server. It also allows a user to select a ROI after the compression is done. This feature is the main contribution of research. The proposed coding method achieves the region scalable coding by using the integer wavelet lifting, successive quantization, and partitioning that rearranges the wavelet coefficients into subsets. Each subset that represents a local area in an image is then separately coded using run-length and entropy coding. In this paper, we will show the benefits of using the proposed technique with examples and simulation results.
Accelerated Joint Image Despeckling Algorithm in the Wavelet and Spatial DomainsCSCJournals
Noise is one of the most widespread problems present in nearly all imaging applications. In spite of the sophistication of the recently proposed methods, most denoising algorithms have not yet attained a desirable level of applicability. This paper proposes a two-stage algorithm for speckle noise reduction jointly in the wavelet and spatial domains. At the first stage, the optimal parameter value of the spatial speckle reduction filter is estimated, based on edge pixel statistics and noise variance. Then the optimized filter is used at the second stage to additionally smooth the approximation image of the wavelet sub-band. A complexity reduction algorithm for wavelet decomposition is also proposed. The obtained results are highly encouraging in terms of image quality which paves the way towards the reinforcement of the proposed algorithm for the performance enhancement of the Block Matching and 3D Filtering algorithm tackling multiplicative speckle noise.
Image Denoising by using Modified SGHP Algorithm IJECEIAES
In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Copy Move Forgery Detection Using GLCM Based Statistical Features ijcisjournal
The features Gray Level Co-occurrence Matrix (GLCM) are mostly explored in Face Recognition and
CBIR. GLCM technique is explored here for Copy-Move Forgery Detection. GLCMs are extracted from all
the images in the database and statistics such as contrast, correlation, homogeneity and energy are
derived. These statistics form the feature vector. Support Vector Machine (SVM) is trained on all these
features and the authenticity of the image is decided by SVM classifier. The proposed work is evaluated on
CoMoFoD database, on a whole 1200 forged and processed images are tested. The performance analysis
of the present work is evaluated with the recent methods.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Supervised Blood Vessel Segmentation in Retinal Images Using Gray level and M...IJTET Journal
The segmentation of membranel blood vessels within the retina may be a essential step in designation of diabetic retinopathy during this paper, gift a replacement methodology for mechanically segmenting blood vessels in retinal pictures. 2 techniques for segmenting retinal blood vessels, supported totally different image process techniques, square measure represented and their strengths and weaknesses square measure compared. This methodology uses a neural network (NN) theme for element classification and gray-level and moment invariants-based options for element illustration. The performance of every algorithmic program was tested on the STARE and DRIVE dataset. wide used for this purpose, since they contain retinal pictures and also the
vascular structures. Performance on each sets of check pictures is healthier than different existing pictures. The methodology
proves particularly correct for vessel detection in STARE pictures. This effectiveness and lustiness with totally different image conditions, is employed for simplicity and quick implementation. This methodology used for early detection of Diabetic Retinopathy (DR)
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
1.4 modern child centered education - mahatma gandhi-2.pptx
Denoising Process Based on Arbitrarily Shaped Windows
1. Huda Al-Ghaib & Reza Adhami
International Journal of Image Processing (IJIP), Volume (9) : Issue (6) : 2015 304
Denoising Process Based on Arbitrarily Shaped Windows
Huda Al-Ghaib Huda.Ghaib@uvu.edu
Assistant Professor/Computer Science/Technology and Computing
Utah Valley University
Orem, 84058, USA
Reza Adhami adhamir@uah.edu
Professor/Electrical and Computer Engineering
University of Alabama in Huntsville
Huntsville, 35899, USA
Abstract
Many factors, such as moving objects, introduce noise in digital images. The presence of noise
affects image quality. The image denoising process works on reconstructing a noiseless image
and improving its quality. When an image has an additive white Gaussian noise (AWGN) then
denoising becomes a challenging process. In our research, we present an improved algorithm for
image denoising in the wavelet domain. Homogenous regions for an input image are estimated
using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied
to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR)
measurements showed that our algorithm provided better results compared with a denoising
algorithm based on a minimum mean square error (MMSE) estimator.
Keywords: Region Merging, Wavelet Transform, Image Denoising, Noise Estimation, Wavelet
Shrinkage Process.
1. INTRODUCTION
Digital images have numerous applications in areas such as medical imaging, biometrics,
robotics, and image navigation [1]. Some examples of medical imaging are computed
tomography scan (CT), magnetic resonance imaging (MRI), ultrasound, X-ray, myocardial
perfusion, and mammography. Medical imaging devices have helped physicians diagnose
different diseases at early stages. In some cases, medical images may be noisy due to factors
such as patient movement during the imaging process [2]-[4]. Noise presented in a digital image
affects its quality. Noise occurs during image acquisition and/ or transmission. In most cases,
noise originates from an unknown source and location. In this case, the noise is assumed to be
an additive white Gaussian noise (AWGN) of unknown mean and variance. Accurate estimation
of noise parameters is a preliminary step in a successful denoising process.
The main goal of the presented research is to provide a method to denoise digital images using
accurate estimations of noise variance. The denoising process is applied on local image patches.
First, an input image is subdivided into a number of patches. For each patch, the local variance is
estimated and used to denoise the image using a wavelet shrinkage denoising algorithm. Noise
estimation and elimination are executed in the wavelet domain.
2. MATERIALS and METHODS
2.1 The Proposed Algorithm
AWGN presented in digital images is of an additive nature. Differentiation of image information
and noise is a challenging procedure. AWGN is characterized by its mean and variance. In most
cases, AWGN is assumed to possess a mean of zero. In our research, wavelet transform is
2. Huda Al-Ghaib & Reza Adhami
International Journal of Image Processing (
applied to decompose an input image of size
magnitude of the vertical and horizontal details is computed to produce an image,
.ܰݔܯ MAG is subdivided into a
computed to estimate the local variance using only the homogenous regions [5].
merging algorithm is explained in detail in
Section 2.3, is applied to denoise the sub image
2.2 Local Noise Estimation Using
An image ܫ is subdivided into ܯݔܯ
as shown in Figure 1. Where
window at the center in ܴ. For each sub window in
satisfied:
1. for .
2. .
where ܯ ൌ 9, ݉ ൌ 3, and ܳ ൌ 9
The variance for the pixels within
adjacent sub windows are merged with
test is computed as:
where is the variance of ݎ. Each sub window is assumed to be of zero mean and the variance
is computed as:
where ܿ is the coefficient within
where ݐ ൌ 0.2. If ݉ ൌ 1 for a given
homogeneity test is performed on every sub wind
shape. When ܳ ൌ 9, there are
possible configurations for ܴ.
Image Processing (IJIP), Volume (9) : Issue (6) : 2015
to decompose an input image of size ܰݔܯ into high and low frequency components. The
magnitude of the vertical and horizontal details is computed to produce an image,
a number of sub images. For each sub image, region merging is
to estimate the local variance using only the homogenous regions [5].
merging algorithm is explained in detail in Section 2.2. A wavelet shrinkage process, explained in
, is applied to denoise the sub image using the estimated variance [6].
Local Noise Estimation Using The Region-Merging Algorithm
ܯݔܯ windows. Next, ܴ window is subdivided into ܳ
are the sub windows each of size ݉݉ݔ
. For each sub window in ܴ, the following two conditions must be
9.
FIGURE 1: Region ܴ of size 9x9.
The variance for the pixels within ݎ is and is considered as the seed sub window in
adjacent sub windows are merged with ݎ if they pass the homogeneity test. The homogeneity
(1)
. Each sub window is assumed to be of zero mean and the variance
(2)
within ݎ. The following condition is applied to test ݄:
݉ ൌ ൜
1, ݂݅ ݄ ൏ ݐ
0, ݁ݏ݅ݓݎ݄݁ݐ
(3)
for a given ݎ, then ݎ is merged with ݎ, otherwise it is discarded. The
homogeneity test is performed on every sub window in ܴ. As a result, final ܴ is of an arbitrary
2ொିଵ
ൌ 256 different configurations for ܴ. Figure 2 shows some
305
into high and low frequency components. The
magnitude of the vertical and horizontal details is computed to produce an image, MAG, of size
ub images. For each sub image, region merging is
to estimate the local variance using only the homogenous regions [5]. The region
. A wavelet shrinkage process, explained in
ܳ sub windows
݉.݉ݔ ݎ is the sub
, the following two conditions must be
and is considered as the seed sub window in ܴ. Other
if they pass the homogeneity test. The homogeneity
. Each sub window is assumed to be of zero mean and the variance
, otherwise it is discarded. The
is of an arbitrary
2 shows some
3. Huda Al-Ghaib & Reza Adhami
International Journal of Image Processing (
The local noise variance for the merged sub windows
where is the global noise variance estimated using
[5]. Equation (4) is applied on each image patch.
FIGURE 2: Local windows with arbitrary size and shape
2.3 The Denoising Algorithm
A wavelet shrinkage denoising operator can be defined as [24]:
Since ݔ is the magnitude of the detail coefficients and is always
as:
The function ܥሺܯሻ must satisfy the following two conditions:
1. Being a piece wise linear function.
2. Being a monotonically non-decreasing function.
ܶ is the threshold value. An accurate estimation for
process. Reference [8] suggested the following mathematical model to compute
ܰ is the signal length and
equation (7) cannot be applied to our algorithm for the following reasons:
1. It uses a non-orthogonal UWT.
2. The shrinkage operation is applied to the magnitudes of the gradi
wavelet coefficients.
Image Processing (IJIP), Volume (9) : Issue (6) : 2015
The local noise variance for the merged sub windows is computed as:
(4)
is the global noise variance estimated using a variation-adaptive evolutionary approach
[5]. Equation (4) is applied on each image patch.
Local windows with arbitrary size and shape; ݎ is the gray-scale region
A wavelet shrinkage denoising operator can be defined as [24]:
(5)
is the magnitude of the detail coefficients and is always , equation (5) can be rewritten
(6)
must satisfy the following two conditions:
Being a piece wise linear function.
decreasing function.
ccurate estimation for ܶ is needed to have an efficient denoising
] suggested the following mathematical model to compute ܶ
(7)
is the standard deviation of the wavelet coefficients. However,
pplied to our algorithm for the following reasons:
orthogonal UWT.
The shrinkage operation is applied to the magnitudes of the gradient coefficients instead of the
306
adaptive evolutionary approach
scale region.
) can be rewritten
to have an efficient denoising
as:
is the standard deviation of the wavelet coefficients. However,
ent coefficients instead of the
4. Huda Al-Ghaib & Reza Adhami
International Journal of Image Processing (
For a white Gaussian noise, the probability distr
characterized by the Rayleigh distribution as [
As a result, there is a direct relationship between
deviations for the Gaussian and Rayleigh distributions respectively. Thus, equation (
rewritten as:
is the probability of noise removal for a particular threshold
Based on Equation (10),
is computed using the histogram of
applied a novel approach based on ACO to achieve an accurate estimation of
2.4 Error Rate for Estimation
A set of images was used as experimental input to test the noise variance estimation algorithms.
These images are: Lena, cameraman, barbara, kodim05, kodim06, kodim07, kodim08, kodim21,
and kodim24, Figure 3 shows the input images. AWGN with different stan
were added to the input images, i.e.,
images of high noise density. Noise variance is estimated using
approach [7]. Table I displays the averaged
and estimated. Table II displays the averaged error rate for the estimated noise variance for the
input images. Even though the added noise possesses high standard deviations, it is obvious
from Tables I and II that the estimation process is of low error rate.
Image Processing (IJIP), Volume (9) : Issue (6) : 2015
For a white Gaussian noise, the probability distribution function of the magnitude of gradients is
characterized by the Rayleigh distribution as [8]:
(8)
As a result, there is a direct relationship between and , where and are the standard
Gaussian and Rayleigh distributions respectively. Thus, equation (
(9)
is the probability of noise removal for a particular threshold ܶ and is computed as:
(10)
for ൌ 0.999, and for ൌ 0.99996
computed using the histogram of and an iterative curve fitting function. In our paper we
applied a novel approach based on ACO to achieve an accurate estimation of [7].
stimation
A set of images was used as experimental input to test the noise variance estimation algorithms.
These images are: Lena, cameraman, barbara, kodim05, kodim06, kodim07, kodim08, kodim21,
and kodim24, Figure 3 shows the input images. AWGN with different standard deviation values
were added to the input images, i.e., and 25.5 respectively. This produced
images of high noise density. Noise variance is estimated using a variation-adaptive evolutionary
approach [7]. Table I displays the averaged normalized values for the noise variance, i.e., added
Table II displays the averaged error rate for the estimated noise variance for the
input images. Even though the added noise possesses high standard deviations, it is obvious
I and II that the estimation process is of low error rate.
FIGURE 3: Input Images.
307
ibution function of the magnitude of gradients is
are the standard
Gaussian and Rayleigh distributions respectively. Thus, equation (7) can be
and is computed as:
99996. In [6] and [8]
and an iterative curve fitting function. In our paper we
].
A set of images was used as experimental input to test the noise variance estimation algorithms.
These images are: Lena, cameraman, barbara, kodim05, kodim06, kodim07, kodim08, kodim21,
dard deviation values
respectively. This produced
adaptive evolutionary
normalized values for the noise variance, i.e., added
Table II displays the averaged error rate for the estimated noise variance for the
input images. Even though the added noise possesses high standard deviations, it is obvious
5. Huda Al-Ghaib & Reza Adhami
International Journal of Image Processing (IJIP), Volume (9) : Issue (6) : 2015 308
TABLE 1: Noise Variance Estimation.
(Added) 0.1 0.078 0.05 0.04 0.01
(Estim.) ACO 0.082 0.068 0.059 0.048 0.028
TABLE 2: Mean and variance of error rate: Comparison for different noise estimation algorithms.
Mean of error rate Variance of error rate
0.1 0.0197 1.2419e-005
0.0784 0.0108 1.2535e-005
0.05 0.0034 1.1162e-005
0.04 0.0055 3.6346e-005
0.01 0.0035 5.3444e-006
3. EXPERIMENTAL RESUTLS
The noisy images, along with the estimated noise variance, are used as inputs for the denoising
algorithm. Wavelet transform is applied to decompose the input image. The algorithm is
performed on the magnitude of the horizontal and vertical details. The algorithm explained in
Section 2.3 is implemented to estimate the local variance for each image patch. The local
denoising process is applied using the denoising algorithm explained earlier in Section 2.3.
Denoising is performed twice using a minimum mean square error (MMSE) estimator and wavelet
shrinkage algorithm respectively. The peak signal to noise ratio (PSNR) is computed for the noisy
and denoised images, and the results are presented in Table III. Notations (1) and (2) in Table III
illustrate wavelet-based denoising based on a MMSE estimator and our algorithm respectively.
From Table III it is obvious that our improved algorithm provided better results compared with the
original algorithm based on using a MMSE estimator [5]. These results showed that MMSE
provided slightly better results in only 10 cases compared with our algorithm. Our algorithm
accuracy is 78.78%.
4. CONCLUSION
This research provided an improved denoising algorithm based on wavelet shrinkage operation.
A region merging algorithm is developed in the wavelet domain to locate the homogenous
regions. The local homogenous regions are applied to estimate the local variance to denoise the
region using the region merging algorithm. Experimental results based on PSNR illustrated in
Table III showed that our improved algorithm provided better results compared with minimum
mean square error (MMSE) in 78.78% of the cases.
5. REFERENCES
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processing at the undergraduate level in engineering, Interactive Collaborative Learning
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[5] I. Kyu and Y. Kim, Wavelet-based denoising with nearly arbitrarily shaped windows, Signal
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