Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization Reconstruction
Authors: 
Hui Huang, Di Xiao, and Jia LiangAuthors Info & Claims
Hui Huang, Di Xiao, and Jia LiangAuthors Info & ClaimsArticle No.: 116, Pages 1 - 22
Abstract
Compressive sensing (CS), a breakthrough technology in image processing, provides a privacy-preserving layer and image reconstruction while performing sensing and recovery processes, respectively. Unfortunately, it still faces high-complexity, low-security, and low-quality reconstruction challenges during image processing. Therefore, this article presents a secure low-complexity CS scheme with preconditioning prior regularization reconstruction. More specifically, the original image is compressed by a low-complexity LFSR-based sparse circulant matrix to obtain measurements. It is worth noting that measurements achieve preliminary distribution equalization through the Tanh sequence to acquire processed measurements. Furthermore, the privacy-preserving edge processing for processed measurements can achieve high security. Finally, preconditioning prior regularization CS reconstruction is designed to improve reconstruction performance. Simulation results and analyses demonstrate that the proposed scheme can achieve low-complexity sampling, high security, and superior reconstruction performance.
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Index Terms
- Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization Reconstruction
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Published In
April 2024
676 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3613617
- Editor:
- Abdulmotaleb El Saddik
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Association for Computing Machinery
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Publication History
Published: 11 January 2024
Online AM: 02 December 2023
Accepted: 25 November 2023
Revised: 20 November 2023
Received: 25 November 2022
Published in TOMM Volume 20, Issue 4
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- National Key R&D Program of China
- National Natural Science Foundation of China
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