Privacy-Assured and Multi-Prior Recovered Compressed Sensing for Image Compression-Encryption Applications

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l(1) norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.