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Single Image Super-Resolution is a classic computer vision problem thatinvolves estimating high-resolution (HR) images from low-resolution (LR) ones.Although deep neural networks (DNNs), especially Transformers forsuper-resolution, have seen significant advancements in recent years,challenges still remain, particularly in limited receptive field caused bywindow-based self-attention. To address these issues, we introduce a group ofauxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SRmethod. The introduced token dictionary could learn prior information fromtraining data and adapt the learned prior to specific testing image through anadaptive refinement step. The refinement strategy could not only provide globalinformation to all input tokens but also group image tokens into categories.Based on category partitions, we further propose a category-basedself-attention mechanism designed to leverage distant but similar tokens forenhancing input features. The experimental results show that our methodachieves the best performance on various single image super-resolutionbenchmarks.

Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary