Sparse coding for image denoising using spike and slab prior

Authors:
Xiaoqiang Lu;Yuan Yuan;Pingkun Yan
Affiliations:
The Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Scienc ...;The Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Scienc ...;The Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Scienc ...
Venue:
Neurocomputing
Year:
2013

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Abstract

Sparse coding is a challenging and promising theme in image denoising. Its main goal is to learn a sparse representation from an over-complete dictionary. How to obtain a better sparse representation from the dictionary is important for the denoising process. In this paper, starting from the classic image denoising problem, a Bayesian-based sparse coding algorithm is proposed, which learns sparse representation with the spike and slab prior. Using the spike and slab prior, the proposed algorithm can achieve accurate prediction performance and effectively enforce sparsity. Experimental results on image denoising have demonstrated that the proposed algorithm can provide better representation and obtain excellent denoising performance.