Local Adaptive Dictionary Based Image Denoising

被引:0
作者
Tang, Yi [1 ]
Yuan, Yuan [1 ]
Yan, Pingkun [1 ]
Li, Xuelong [1 ]
Zhou, Hui [2 ]
Li, Luoqing [2 ]
机构
[1] Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian, Shaanxi, Peoples R China
[2] Hubei Univ, Fac Math & Comp Sci, Wuhan, Peoples R China
来源
2011 FIRST ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR) | 2011年
基金
中国国家自然科学基金;
关键词
image denosing; adaptive; sparse coding; local weighted regression; SPARSE REPRESENTATION; LEARNED DICTIONARIES; ALGORITHMS; EDGES;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, the problem of balancing the noise removing and the image details preserving is considered. To remove noise adaptively, local dictionaries and sparse coding techniques are used. For a noised image patch, the local dictionary corresponding to it and the sparse coding technique are used to generate the sparse coding vector of the given patch. Then the noise of the given patch can be removed without any information on noise level by setting all components be zero but preserving largest component of the sparse coding vector. Because too much information on image details are removed with noise by the above process, a local weighted regression is adopted to refine the denoising image with the help of the information on the local geometry structure of noised image. Various experiments have been accomplished and prove our method to be effective in balancing the noise removing and the image details preserving.
引用
收藏
页码:412 / 416
页数:5
相关论文
共 22 条
[11]   Wedgelets: Nearly minimax estimation of edges [J].
Donoho, DL .
ANNALS OF STATISTICS, 1999, 27 (03) :859-897
[12]   Image denoising via sparse and redundant representations over learned dictionaries [J].
Elad, Michael ;
Aharon, Michal .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (12) :3736-3745
[13]   Local adaptivity to variable smoothness for exemplar-based image regularization and representation [J].
Kervrann, Charles ;
Boulanger, Jerome .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2008, 79 (01) :45-69
[14]   Optimal spatial adaptation for patch-based image denoising [J].
Kervrann, Charles ;
Boulanger, Jerome .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (10) :2866-2878
[15]   Dictionary learning algorithms for sparse representation [J].
Kreutz-Delgado, K ;
Murray, JF ;
Rao, BD ;
Engan, K ;
Lee, TW ;
Sejnowski, TJ .
NEURAL COMPUTATION, 2003, 15 (02) :349-396
[16]  
LESAGE L, 2005, IEEE INT C AC SPEECH
[17]   Learning multiscale sparse representations for image and video restoration [J].
Mairal, Julien ;
Sapiro, Guillermo ;
Elad, Michael .
MULTISCALE MODELING & SIMULATION, 2008, 7 (01) :214-241
[18]  
Mallat S., 2005, SIAM J MULTISCALE MO
[19]   Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors [J].
Moulin, P ;
Liu, J .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1999, 45 (03) :909-919
[20]  
Simoncelli E., 1996, P INT C IM PROC SEP