NDLI: An efficient learned dictionary and its application to non-local denoising

Content Provider	IEEE Xplore Digital Library
Author	Shutao Li Leyuan Fang
Copyright Year	2010
Description	Author affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China (Shutao Li; Leyuan Fang)
Abstract	This paper proposes a new learned dictionary for sparse representation of given data and suggests a way to apply it to non-local denoising. First, a quad-tree structure is efficiently embedded into a sparse dictionary model. This enables the dictionary to discover the complex structures in the given data and to be easily employed to high dimensional data. Besides, we propose a joint 3-D operation to exploit the correlations among the similar blocks, as the non-local denoising model assumes that there exist mutually similar blocks in nature images. This 3-D operation is achieved by a simple concatenation of the similar patches to a single vector and training the proposed dictionary on it. The experimental results indicate that our approach is competitive with several well known denoising techniques in terms of both PSNR and visual quality.
Starting Page	1945
Ending Page	1948
File Size	537284
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5652718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Noise reduction Joints Image denoising Training Approximation algorithms Matching pursuit algorithms K-SVD Sparse representation Dictionary learning Non-local denoising
Content Type	Text
Resource Type	Article

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