Bayesian Compressive Sensing of Images Based on Effective Blocking

Abstract:

The traditional reconstruction method of Compressive Sensing (CS) was mostly depended on L₁-norm linear regression model. And here we propose Bayesian Compressive Sensing (BCS) to reconstruct the signal. It provides posterior distribution of the parameter rather than point estimate, so we can get the uncertainty of the estimation to optimize the data reconstruction process adaptively. In this paper, we employ hierarchical form of Laplace prior, and aiming at improving the efficiency of reconstruction, we segment image into blocks, employ various sample rates to compress different kinds of block and utilize relevance vector machine (RVM) to sparse signal in the reconstruction process. At last, we provide experimental result of image, and compare with the state-of-the-art CS algorithms, it demonstrating the superior performance of the proposed approach.

• Periodical: Applied Mechanics and Materials (Volumes 599-601)
• Pages: 1411-1415
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.599-601.1411
• Online since: August 2014
• Authors: Yan Hai Wu, Meng Xin Ma*, Nan Wu, Jing Wang
• Keywords: Bayesian Estimation, Block Compressive Sensing, Hierarchical Prior Model, Laplace Prior, Relevance Vector Machine

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