结合自适应字典学习的稀疏贝叶斯重构

doi:10.3969/j.issn.1001-2400.2016.04.001

Abstract

Abstract:

Bayesian compressive sensing (BCS), a kind of compressive sensing algorithm based on statistical analysis, uses information correspondence to get robust performance in image reconstruction. But it depends on image sparsity strongly. In order to solve further level sparsity of BCS, this paper presents a novel self-adaptive Bayesian compressive sensing algorithm combined with redundancy self-adaptive dictionary learning. The algorithm firstly decomposes an image into local patches and builds the dictionary from the iterating transition image. Then the image is represented by this dictionary space. Finally, the image is reconstructed using the sparse Bayesian learning algorithm. Experimental results show that the proposed algorithm obtains deep sparse representation and improves image reconstruction quality.

Key words: sparse Bayesian learning, self-adaptive dictionary, Bayesian compressive sensing

WANG Yong;QIAO Qianqian;YANG Xiaoyu;XU Wenjuan;JIA Zheng;CHEN Chuchu;GAO Quanxue. Sparse Bayesian reconstruction combined with self-adaptive dictionary learning[J].Journal of Xidian University, 2016, 43(4): 1-4+122.

References

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Sparse Bayesian reconstruction combined with self-adaptive dictionary learning

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