Similarity validation based nonlocal means image denoising
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This document discusses a method to improve the performance of nonlocal means image denoising. It proposes a pre-processing step that eliminates dissimilar patches from the search neighborhood before denoising. A hard threshold is calculated based on the distribution of distances between noisy similar patches. The proposed Similarity Validation Based Nonlocal Means (NLM-SVB) method shows improved PSNR and SSIM over nonlocal means and other recent variations when retrieving a denoised image.
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1070-9908 (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/LSP.2015.2465291, IEEE Signal Processing Letters
1
Similarity Validation Based Nonlocal Means
Image Denoising
Mina Sharifymoghaddam, Student Member, IEEE, Soosan Beheshti, Senior Member, IEEE,
Pegah Elahi, Student Member, IEEE and Masoud Hashemi, Student Member, IEEE
Abstract
Nonlocal means is one of the well known and mostly used image denoising methods. The conventional nonlocal
means approach uses weighted version of all patches in a search neighbourhood to denoise the center patch. However,
this search neighbourhood can include some dissimilar patches. In this paper, we propose a pre-processing hard
thresholding algorithm that eliminates those dissimilar patches. Consequently, the method improves the performance
of nonlocal means. The threshold is calculated based on the distribution of distances of noisy similar patches. The
method denoted by Similarity Validation Based Nonlocal Means (NLM-SVB) shows improvement in terms of PSNR
and SSIM of the retrieved image in comparison with nonlocal means and some recent variations of nonlocal means.
Index Terms
Image denoising, Nonlocal means, Noise invalidation, Hard thresholding
I. INTRODUCTION
As digital imaging technologies become more advanced, the issue of image denoising still remains as a challenging
stage. Removing additive noise is an essential pre-processing step in the majority of image processing techniques
such as classification and object recognition, or it can be used for the purpose of improving image visual quality.
Some of the earliest methods of denoising are simple averaging filters such as mean, median, Gaussian smoothing
filters, and bilateral filters [1]. There are methods that transform data to other bases for the purpose of denoising
such as wavelet or curvelet based methods [2].
The concentration of this paper is on nonlocal means methods (NLM) that are preferred when algorithm com-
plexity is an issue. Most local methods only consider a local patch around the target pixel, assuming adjacent pixels
tend to have similar patches. On the other hand, nonlocal means takes advantage of existence of a pattern or similar
Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must
be obtained from the IEEE by sending a request to pubs-permissions@ieee.org.
M. Sharifymoghaddam and S. Beheshti are with the Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON,
Canada e-mail: (m22shari@ryerson.ca, soosan@ryerson.ca).
P. Elahi is with Altus Group, Toronto, ON, Canada e-mail: (p.elahi81@gmail.com).
M. Hashemi is with Sunnybrook Health Science Center, Toronto, ON, Canada e-mail: (hashemi.masoud@sunnybrook.ca).
August 4, 2015 DRAFT](https://image.slidesharecdn.com/similarityvalidationbasednonlocalmeansimagedenoising-151124120417-lva1-app6892/85/Similarity-validation-based-nonlocal-means-image-denoising-1-320.jpg)
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Ja3615721579
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.
FACE EXPRESSION IDENTIFICATION USING IMAGE FEATURE CLUSTRING AND QUERY SCHEME...
Web mining techniques are used to analyze the web page contents and usage details. Human facial
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Classification techniques are used to assign the facial annotation. Supervised or semi-supervised machine learning
techniques are used to train the classification models. Facial images with labels are used in the training process.
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mining weakly labeled facial images available on the World Wide Web (WWW). Unsupervised label refinement
(ULR) approach is used for refining the labels of web facial images with machine learning techniques. ULR
scheme is used to enhance the label quality using graph-based and low-rank learning approach. The training phase
is designed with facial image collection, facial feature extraction, feature indexing and label refinement learning
steps. Similar face retrieval and voting based face annotation tasks are carried out under the testing phase.
Clustering-Based Approximation (CBA) algorithm is applied to improve the scalability. Bisecting K-means
clustering based algorithm (BCBA) and divisive clustering based algorithm (DCBA) are used to group up the
facial images. Multi step Gradient Algorithm is used for label refinement process. The web face annotation scheme
is enhanced to improve the label quality with low refinement overhead. Noise reduction is method is integrated
with the label refinement process. Duplicate name removal process is integrated with the system. The indexing
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facial image relevancy issues.
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binarization of the enhanced image to highlight the foreground information, using Otsu
thresholding algorithm. In the second phase Segmentation is carried out using Drop Fall and
WaterReservoir approaches, to obtain sampled characters, which can be used in later stages of
OCR. The system showed good results when tested on the nearly 150 samples of varying
degraded epigraphic images and works well giving better enhanced output for, 4x4 mask size
for Median filter, 2x2 mask size for Gaussian blur, 4x4 mask size for Mean and Bilateral filter.
The system can effectively sample characters from enhanced images, giving a segmentation rate of 85%-90% for Drop Fall and 85%-90% for Water Reservoir techniques respectively
Journals analysis ppt
The document summarizes three journal articles about grid and cloud computing.
The first journal investigates the benefits of grid computing technologies for high-performance computing. It uses a case study approach and experimental methodology. Three scenarios are modeled to test average job response times.
The second journal aims to develop a prototype integrating grid technologies with NASA's web GIS software. It determines integration models and system architecture through data gathering and analysis. Components are developed and tested within a virtual organization environment.
The third journal comprehensively compares grid and cloud computing concepts from different perspectives. It collects data through observations and content analysis of definitions to ensure cloud computing is not just a renaming of grid computing.
IEEE MultiMedia 2016 Title and Abstract
For Details, Contact TSYS Academic Projects.
Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
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