Optimized truncation model for adaptive compressive sensing acquisition of images

Abstract

The sparsity of the input signal is important for compressive sensing (CS) reconstruction in CS system. In this paper, we establish an optimized truncation model to determine the number of the sparsified coefficients to be truncated in CS acquisition according to the sampling rate. The proposed truncation model suits for signals of any dimension. With the truncation model, the sparsity of the signal can be optimized by properly truncating the small elements of the sparsified coefficients. Furthermore we propose an adaptive CS acquisition solution based on the truncation model to reduce the noise folding effect. The proposed solution is verified for CS acquisition of natural images. Simulation results show that the proposed solution achieves significant improvement of the reconstructed image quality by 0.7∞1.4 dB on average compared with existing solutions.