Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
CoRR(2024)
摘要
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 Adapeive 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.
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