ICRICS: Iterative Compensation Recovery for Image Compressive Sensing
This work addresses reconstruction performance in image compressive sensing systems, offering an incremental improvement by enhancing existing methods with a negative feedback mechanism.
The paper tackled the problem of image compressive sensing by introducing a closed-loop framework with negative feedback to correct recovery errors in existing systems, resulting in a maximum increase of 4.36 dB in average peak signal-to-noise ratio and 0.034 in average structural similarity on one dataset.
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.