Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image Restoration

Transformer-based approaches have achieved promising performance in image restoration tasks, given their ability to model long-range dependencies, which is crucial for recovering clear images. Though diverse efficient attention mechanism designs have addressed the intensive computations associated with using transformers, they often involve redundant information and noisy interactions from irrelevant regions by considering all available tokens. In this work, we propose an Adaptive Sparse Transformer (AST) to mitigate the noisy interactions of irrelevant areas and remove feature redundancy in both spatial and channel domains. AST comprises two core designs, i.e., an Adaptive Sparse Self-Attention (ASSA) block…