Compressed image restoration via deep deblocker driven unified framework

Abstract As a typical ill-posed problem, JPEG compressed image restoration (CIR) aims to recover a high-quality (HQ) image from the compressed version. Although many model-based and learning-based methods have been proposed for conventional image restoration (IR), proposing a general and effective framework for various CIR tasks is still a challenging work. The model-based methods are flexible for handling different IR tasks, but they suffer from high complexity and the difficulty in designing sophisticated priors. The learning-based methods have shown promising results in various IR tasks. However, most of them need to retrain their models for each IR task separately, which sacrifices methods’ flexibility. In this paper, we propose a novel and high-performance deep deblocker driven unified framework to flexibly address various CIR tasks without retraining. 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. Since simultaneously modeling the data fidelity term and implicit priors via deblockers is necessary, 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, and it could also contribute to other deblocking methods to obtain better flexibility. The effectiveness of the proposed method is demonstrated both visually and quantitatively.