标题:Adaptive sparse coding on PCA dictionary for image denoising
作者:Liu, Qian; Zhang, Caiming; Guo, Qiang; Xu, Hui; Zhou, Yuanfeng
通讯作者:Zhang, Caiming
作者机构:[Liu, Qian; Zhang, Caiming; Guo, Qiang; Xu, Hui; Zhou, Yuanfeng] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.; [Zhang, Caim 更多
会议名称:Computational Visual Media Conference (CVM)
会议日期:APR 16-17, 2015
来源:VISUAL COMPUTER
出版年:2016
卷:32
期:4
页码:535-549
DOI:10.1007/s00371-015-1087-x
关键词:Image denoising; Sparse coding; Iterative shrinkage; Principal component; analysis
摘要:Sparse coding is a popular technique in image denoising. However, owing to the ill-posedness of denoising problems, it is difficult to obtain an accurate estimation of the true code. To improve denoising performance, we collect the sparse coding errors of a dataset on a principal component analysis dictionary, make an assumption on the probability of errors and derive an energy optimization model for image denoising, called adaptive sparse coding on a principal component analysis dictionary (ASC-PCA). The new method considers two aspects. First, with a PCA dictionary-related observation of the probability distributions of sparse coding errors on different dimensions, the regularization parameter balancing the fidelity term and the nonlocal constraint can be adaptively determined, which is critical for obtaining satisfying results. Furthermore, an intuitive interpretation of the constructed model is discussed. Second, to solve the new model effectively, a filter-based iterative shrinkage algorithm containing the filter-based back-projection and shrinkage stages is proposed. The filter in the back-projection stage plays an important role in solving the model. As demonstrated by extensive experiments, the proposed method performs optimally in terms of both quantitative and visual measurements.
收录类别:CPCI-S;EI;SCOPUS;SCIE
WOS核心被引频次:6
Scopus被引频次:6
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928975271&doi=10.1007%2fs00371-015-1087-x&partnerID=40&md5=7dab66ac898faa6743221dc242d1fe81
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