Bayesian method for image recovery from block compressive sensing

Wijewardhana, U. L.; Codreanu, M.; Latva-aho, M. (2017-03-06)
 
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https://doi.org/10.1109/ACSSC.2016.7869064

Wijewardhana, U. L.
Codreanu, M.
Latva-aho, M.
Institute of Electrical and Electronics Engineers
06.03.2017

U. L. Wijewardhana, M. Codreanu and M. Latva-aho, "Bayesian method for image recovery from block compressive sensing," 2016 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2016, pp. 379-383. doi: 10.1109/ACSSC.2016.7869064

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doi:https://doi.org/10.1109/ACSSC.2016.7869064
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Abstract

We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce the blocking artifacts. Specifically, we derive an iterative image reconstruction method, where a small number of adjacent measurement blocks are jointly processed for recovering an image block. For this purpose, we propose a novel sparse Bayesian learning (SBL) algorithm.

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