Soft Consistency Reconstruction: A Robust 1-bit Compressive Sensing Algorithm
Xiao Cai, Zhaoyang Zhang, Huazi Zhang, Chunguang Li

TL;DR
This paper introduces Soft Consistency Reconstructions (SCRs), a new class of algorithms for 1-bit compressive sensing that enhance noise robustness by redesigning the objective function with soft decision criteria.
Contribution
The paper proposes SCRs with re-designed consistency criteria, significantly improving noise robustness in 1-bit CS recovery compared to existing methods.
Findings
SCRs outperform existing algorithms in noisy environments
SCRs maintain comparable performance in high SNR conditions
Soft decisions improve robustness in low SNR regimes
Abstract
A class of recovering algorithms for 1-bit compressive sensing (CS) named Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS recovery is essentially an optimization problem, we endeavor to improve the characteristics of the objective function under noisy environments. With a family of re-designed consistency criteria, SCRs achieve remarkable counter-noise performance gain over the existing counterparts, thus acquiring the desired robustness in many real-world applications. The benefits of soft decisions are exemplified through structural analysis of the objective function, with intuition described for better understanding. As expected, through comparisons with existing methods in simulations, SCRs demonstrate preferable robustness against noise in low signal-to-noise ratio (SNR) regime, while maintaining comparable performance in high SNR regime.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · Blind Source Separation Techniques · Microwave Imaging and Scattering Analysis
