Backtracking-based iterative regularization method for image compressive sensing recovery
type: Item
instance of: scholarly article
title: Backtracking-based iterative regularization method for image compressive sensing recovery
zbMATH Open document ID: 1461.68243
author: Lingjun Liu
author: Zhonghua Xie
author: Jiuchao Feng
published in: Algorithms
publication date: 2018-08-20T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 68P30
zbMATH DE Number: 6920552
zbMATH Keywords: iterative shrinkage-thresholding (IST)
zbMATH Keywords: backtracking
zbMATH Keywords: compressive sensing (CS)
zbMATH Keywords: nonlocal regularization
describes a project that uses: TwIST
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a10010007
OpenAlex ID: W2568895469
cites work: Compressive Imaging Using Approximate Message Passing and a Markov-Tree Prior
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DOI: 10.3390/A10010007
description: scientific article; zbMATH DE number 6920552