Adaptive sparse coding on PCA dictionary for image denoising

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• 作者：Qian Liu ; Caiming Zhang ; Qiang Guo ; Hui Xu ; Yuanfeng Zhou
• 关键词：Image denoising ; Sparse coding ; Iterative shrinkage ; Principal component analysis
• 刊名：The Visual Computer
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：32
• 期：4
• 页码：535-549
• 全文大小：3,275 KB
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• 作者单位：Qian Liu (1)
  Caiming Zhang (1) (2)
  Qiang Guo (1) (2)
  Hui Xu (1)
  Yuanfeng Zhou (1)
  
  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China
  2. Shandong Provincial Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan, 250014, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Graphics
  Computer Science, general
  Artificial Intelligence and Robotics
  Image Processing and Computer Vision
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-2315

文摘

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.