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Compared to CNN-based methods, Transformer-based methods achieve impressiveimage restoration outcomes due to their abilities to model remote dependencies.However, how to apply Transformer-based methods to the field of blindsuper-resolution (SR) and further make an SR network adaptive to degradationinformation is still an open problem. In this paper, we propose a newdegradation-aware self-attention-based Transformer model, where we incorporatecontrastive learning into the Transformer network for learning the degradationrepresentations of input images with unknown noise. In particular, we integrateboth CNN and Transformer components into the SR network, where we first use theCNN modulated by the degradation information to extract local features, andthen employ the degradation-aware Transformer to extract global semanticfeatures. We apply our proposed model to several popular large-scale benchmarkdatasets for testing, and achieve the state-of-the-art performance compared toexisting methods. In particular, our method yields a PSNR of 32.43 dB on theUrban100 dataset at $\times$2 scale, 0.94 dB higher than DASR, and 26.62 dB onthe Urban100 dataset at $\times$4 scale, 0.26 dB improvement over KDSR, settinga new benchmark in this area. Source code is available at:https://github.com/I2-Multimedia-Lab/DSAT/tree/main.

Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution