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

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

6 Oct 2023arXiv:2310.04180archive 2025-07-28

Qingguo Liu, Pan Gao, Kang Han, Ningzhong Liu, Wei Xiang

Compared to CNN-based methods, Transformer-based methods achieve impressive image restoration outcomes due to their abilities to model remote dependencies. However, how to apply Transformer-based methods to the field of blind super-resolution (SR) and further make an SR network adaptive to degradation information is still an open problem. In this paper, we propose a new degradation-aware self-attention-based Transformer model, where we incorporate contrastive learning into the Transformer network for learning the degradation representations of input images with unknown noise. In particular, we integrate both CNN and Transformer components into the SR network, where we first use the CNN modulated by the degradation information to extract local features, and then employ the degradation-aware Transformer to extract global semantic features. We apply our proposed model to several popular large-scale benchmark datasets for testing, and achieve the state-of-the-art performance compared to existing methods. In particular, our method yields a PSNR of 32.43 dB on the Urban100 dataset at ×2 scale, 0.94 dB higher than DASR, and 26.62 dB on the Urban100 dataset at ×4 scale, 0.26 dB improvement over KDSR, setting a new benchmark in this area. Source code is available at: https://github.com/I2-Multimedia-Lab/DSAT/tree/main.

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Blind Super-Resolution Contrastive Learning Image Restoration Image Super-Resolution Super-Resolution

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Absolute Position Encodings Adam Attention BPE Contrastive Learning Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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