Deep Deblocker Driven Adaptive Iteration Scheme for Compressed Image Recovery

It is challenging to propose a flexible and effective framework for various JPEG compressed image recovery (CIR) tasks. In this paper, we propose a novel deep deblocker-driven adaptive iteration scheme, which can quickly and flexibly address various CIR tasks. First, a novel fidelity (NF) is introduced into CIR, and then the CIR problem is divided into inversion and deblocking subproblems by our improved split Bregman iteration (ISBI) algorithm. Next, we design a set of compact yet effective deep deblockers. These deblockers are used as implicit priors and also used for NF in the CIR problem. The convergence of our method is proved as well. To the best of our knowledge, our method is the first work to use deblockers as implicit priors. Extensive experiments demonstrate the effectiveness of our CIR method.