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

翻译：单图像超分辨率是一个经典的计算机视觉问题，旨在从低分辨率图像估计高分辨率图像。尽管近年来深度神经网络（尤其是用于超分辨率的Transformer）取得了显著进展，但基于窗口的自注意力机制导致的感受野受限问题仍然存在。为解决此问题，我们引入一组辅助自适应令牌字典到超分辨率Transformer中，并提出ATD-SR方法。该令牌字典能够从训练数据中学习先验信息，并通过自适应细化步骤将学习到的先验适配到特定测试图像。细化策略不仅能为所有输入令牌提供全局信息，还能将图像令牌分组为不同类别。基于类别划分，我们进一步提出一种基于类别的自注意力机制，旨在利用远距离但相似的令牌来增强输入特征。实验结果表明，我们的方法在多个单图像超分辨率基准测试中取得了最佳性能。