Dual Circle Contrastive Learning-Based Blind Image Super-Resolution

Blind image super-resolution (BISR) aims to construct high-resolution image from low-resolution (LR) image that contains unknown degradation. Although the previous methods demonstrated impressive performance by introducing the degradation representation in BISR task, there still exist two problems in most of them. First, they ignore the degradation characteristics of different image regions when generating degradation representation. Second, they lack effective supervision on the generation of both degradation representation and super-resolution result. To solve these problems, we propose the dual circle contrastive learning (DCCL) with the high-efficiency modules to implement BISR. In our proposed method, we design the degradation extraction network to obtain the degradation representations from different texture regions of LR image. Meanwhile, we propose DCCL coupled with the degrading network to guarantee the obtained degradation representation to contain the degradation of LR image as much as possible. The application of DCCL also makes the SR results contain degradation as little as possible. Additionally, we develop an information distillation module for our proposed BISR model to guarantee the SR images with high quality. The experimental results demonstrate that our proposed method achieves the state-of-the-art BISR performance.