Secure Low-complexity Compressive Sensing with Preconditioning Prior Regularization Reconstruction

Abstract

Compressive sensing (CS), a breakthrough technology in image processing, provides a privacy-preserving layer and image reconstruction while performing sensing and recovery processes, respectively. Unfortunately, it still faces high-complexity, low-security, and low-quality reconstruction challenges during image processing. Therefore, this article presents a secure low-complexity CS scheme with preconditioning prior regularization reconstruction. More specifically, the original image is compressed by a low-complexity LFSR-based sparse circulant matrix to obtain measurements. It is worth noting that measurements achieve preliminary distribution equalization through the Tanh sequence to acquire processed measurements. Furthermore, the privacy-preserving edge processing for processed measurements can achieve high security. Finally, preconditioning prior regularization CS reconstruction is designed to improve reconstruction performance. Simulation results and analyses demonstrate that the proposed scheme can achieve low-complexity sampling, high security, and superior reconstruction performance.