ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

TL;DR

This paper introduces ICRICS, a novel closed-loop iterative compensation method for image compressive sensing that enhances reconstruction quality by incorporating negative feedback, outperforming existing approaches.

Contribution

It proposes a negative feedback based iterative compensation framework for image compressive sensing, improving reconstruction accuracy over traditional open-loop methods.

Findings

Outperforms 10 competing methods in reconstruction quality.

Maximum PSNR improvement of 4.36 dB observed.

Maximum SSIM increase of 0.034 achieved.

Abstract

Closed-loop architecture is widely utilized in automatic control systems and attain distinguished performance. However, classical compressive sensing systems employ open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing (ICRICS) is proposed by introducing closed-loop framework into traditional compresses sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding negative feedback structure. Theory analysis on negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competition approaches in reconstruction performance. The maximum increment of average peak signal-to-noise ratio is 4.36 dB and the maximum increment of average structural similarity is 0.034 on one dataset. The proposed method based on negative feedback mechanism can efficiently correct the recovery error in the existing systems of image compressive sensing.