Non-local sparse regularization model with application to image denoising

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• 作者：Ning He ; Jin-Bao Wang ; Lu-Lu Zhang ; Guang-Mei Xu…
• 关键词：Image denoising ; Non ; local means ; Sparse coding ; Regularization ; Self ; similarity
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：75
• 期：5
• 页码：2579-2594
• 全文大小：2,216 KB
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• 作者单位：Ning He (1)
  Jin-Bao Wang (1)
  Lu-Lu Zhang (1)
  Guang-Mei Xu (1)
  Ke Lu (2)
  
  1. Beijing Key Laboratory of Information Service Engineering, College of Information Technology, Beijing Union University, Beijing, 100101, China
  2. University of Chinese Academy of Sciences, Beijing, 100049, China
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

We study problems related to denoising of natural images corrupted by Gaussian white noise. Important structures in natural images such as edges and textures are jointly characterized by local variation and nonlocal invariance. Both provide valuable schemes in the regularization of image denoising. In this paper, we propose a framework to explore two sets of ideas involving on the one hand, locally learning a dictionary and estimating the sparse regularization signal descriptions for each coefficient; and on the other hand, nonlocally enforcing the invariance constraint by introducing patch self-similarities of natural images into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image denoising algorithm; its efficient implementation is discussed. Experimental results from image denoising tasks of synthetic and real noisy images show that the proposed method outperforms the state-of-the-art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.