Dual contrastive attention-guided deformable convolutional network for single image super-resolution

With its powerful ability to model geometric transformations, the deformable convolutional network brings great improvements for single image super-resolution (SISR). Nevertheless, its location-variant sampling method leads to an escalation in spatial variance as the deformable convolutional layers are stacked, consequently resulting in limited performance. Hence, we propose a novel and effective approach called dual contrastive attention-guided deformable convolutional network (DCADCN) for SISR modeling. Specifically, we propose an attention-guided deformable convolutional module with joint inner and external attention mechanisms to fully exploit the correspondences between input and deformation features and preserve spatial characteristics to the extent possible. Additionally, we propose a dual mixed feature extractor consisting of two parallel sub-paths. This design allows for the learning of diverse and complementary spatial features. Furthermore, contrastive learning is applied to further amplify the role of key features and mitigate the interference of noisy features. Extensive experimental results demonstrate that DCADCN is capable of effectively handling classic SISR, SISR with blind noise, and real-world SISR tasks. Moreover, our method achieves comparable or even better performance with lower computational cost compared to state-of-the-art methods.