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Algorithms 2017, 10(1), 7; doi:10.3390/a10010007

Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
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Academic Editor: Bruno Carpentieri
Received: 13 October 2016 / Revised: 21 December 2016 / Accepted: 4 January 2017 / Published: 6 January 2017
(This article belongs to the Special Issue Data Compression, Communication Processing and Security 2016)
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Abstract

This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques. View Full-Text
Keywords: iterative shrinkage-thresholding (IST); backtracking; compressive sensing (CS); nonlocal regularization iterative shrinkage-thresholding (IST); backtracking; compressive sensing (CS); nonlocal regularization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, L.; Xie, Z.; Feng, J. Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery. Algorithms 2017, 10, 7.

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