DocumentCode :
234821
Title :
Image Denoising Using Low-Rank Dictionary and Sparse Representation
Author :
Tao Li ; Weiwei Wang ; Long Xu ; Xiangchu Feng
Author_Institution :
Sch. of Math. & Stat., Xidian Univ., Xi´an, China
fYear :
2014
fDate :
15-16 Nov. 2014
Firstpage :
228
Lastpage :
232
Abstract :
In this paper, we propose an image denoising model by using low-rank dictionary and sparse representation (LRSR). The K-SVD algorithm learns a universal dictionary for all patches in an image and the NLM exploits similarities of nonlocal patches, both achieve effective denoising performance. Motivated by these methods, we propose to use a low-rank dictionary for each cluster of similar patches and the dictionary is used to simultaneously produce sparse representations of all patches in the cluster. Our algorithm has two advantages. The first one is, we use a dictionary particular to each cluster of similar patches so that the dictionary can exploit the peculiar structure underlying the cluster and better adapts to the cluster. The second, we represent the similar patches in a cluster simultaneously by the dictionary so that we can impose a structured sparsity to make full use of similarities of these patches and get better restoration quality. Experimental results show that our method performs better than or on par with the state-of-the-art denoising methods such as BM3D and TDNL.
Keywords :
image denoising; image representation; image restoration; singular value decomposition; BM3D; K-SVD algorithm; LRSR; NLM; TDNL; denoising performance; image denoising model; low-rank dictionary; nonlocal patches; restoration quality; sparse representation; state-of-the-art denoising methods; Dictionaries; Image denoising; Image edge detection; Noise; Noise level; Noise reduction; Image denoising; Low-rank dictionary learning; Nonlocal similarity; Sparse representation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4799-7433-7
Type :
conf
DOI :
10.1109/CIS.2014.56
Filename :
7016889
Link To Document :
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