Various Degradation: Dual Cross-Refinement Transformer for Blind Sonar Image Super-Resolution

Deep learning-based methods have achieved remarkable results in super-resolution (SR) of sonar images. However, most existing methods only consider simple bicubic downsampling degradation, and SR networks suitable for natural images may not be suitable for sonar images. Therefore, they perform poorly on sonar images with unknown degradation parameters in real-world scenarios (i.e., blind scenario). To address these issues, we propose a dual cross-refinement transformer (DCRT) for blind SR of sonar images. DCRT first constructs a large-scale degradation space based on the sonar image imaging mechanism. More importantly, we randomly sample the task-level training information to make DCRT robust on different SR tasks, thereby enhancing the blind SR capability of the network. Then, DCRT focuses on image features than domain features through spatial-channel self-attention cross-fusion block (S-C-SACFB), so the domain gap between the training and testing data can be reduced. Meanwhile, S-C-SACFB effectively combines inter-attention (I-A) and high-frequency enhancement residual block (HFERB) to enhance the network's ability to extract high-frequency features while suppressing speckle noise in sonar images. Finally, DCRT uses global residual connections to generate high-resolution (HR) sonar images. A large number of experiments at different SR scale show that DCRT outperforms the state-of-the-art methods in both quantitative and qualitative aspects.