Boosting Degradation Representation Learning for Blind Image Super-Resolution

In most convolutional neural networks-based super-resolution (SR) methods, the degradation assumptions are fixed and known (e.g., bicubic degradation). When applying them to real-world degraded images, the mismatch between the actual degradation and the assumptions can significantly degrade the performance of SR methods. To solve the problem of SR method performance degradation when degradation mismatch occurs, a new blind SR method is proposed, which can unsupervisedly learn the degradation information of degraded images and use the learned degradation information to efficiently modulate the SR network. Firstly, an unsupervised implicit degradation representation learning method is designed based on contrastive learning, which can extract accurate degradation information without relying on sample queues. It is also proposed to use different images that have gone through the same degradation as positive sample pairs, which overcomes the effect of image content similarity on the learned degradation, and effectively enhances the expressive power of the implicit degradation estimator. Subsequently, a degradation-guided modulation super-resolution network containing a global modulation block and a channel fusion modulation block is designed. It is combined with the degradation information and can flexibly deal with various kinds of degradations to realize the final SR recovery. The experiments are compared with various representative methods on four standard datasets, and the results show that the PSNR on Urban100 dataset is improved by 0.85 dB, 0.87 dB and 0.32 dB respectively over the baseline model DASR (degradation aware for blind super-resolution) for three different magnifications of ×2, ×3 and ×4. Both the quality of the final extracted degradation characterization information and reconstructed SR images are greatly improved over the DASR.