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Image denoising using orthogonal locality preserving projections

DSpace at IIT Bombay

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Title Image denoising using orthogonal locality preserving projections
 
Creator SHIKKENAWIS, G
MITRA, SK
RAJWADE, A
 
Subject WAVELET SHRINKAGE
FACE RECOGNITION
QUALITY ASSESSMENT
ESTIMATORS
SPARSITY
SCHEMES
DOMAIN
image denoising
orthogonal locality preserving projections
dictionary learning
 
Description Image denoising approaches that learn spatially adaptive dictionaries from the observed noisy image have gathered a lot of attention in the past decade. These methods rely on the hypothesis that patches from the underlying clean image can be expressed as sparse linear combinations of these dictionary vectors (bases). We present a framework for inferring an orthonormal set of dictionary vectors using orthogonal locality preserving projection (OLPP). This ensures that patches that are similar in the noisy image should produce similar coefficients when projected in the OLPP domain. Unlike other projection methods, the locality preserving property of OLPP automatically groups similar patches together during inference of the basis. Hence, only one global orthonormal basis suffices to sparsely represent patches from a large subimage or a large portion of the image. The proposed amalgamation of the sparsity and global dictionary make the current approach more suitable for an image denoising task with reduced computational complexity. Experiments on several benchmark datasets made it clear that the proposed method is capable of preserving fine textures while denoising an image, on par with or surpassing several state-of-the-art methods for gray-scale and color images. (C) 2015 SPIE and IS&T
 
Publisher IS&T & SPIE
 
Date 2016-01-15T09:48:10Z
2016-01-15T09:48:10Z
2015
 
Type Article
 
Identifier JOURNAL OF ELECTRONIC IMAGING, 24(4)
1017-9909
1560-229X
http://dx.doi.org/10.1117/1.JEI.24.4.043018
http://dspace.library.iitb.ac.in/jspui/handle/100/18291
 
Language en