  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.
