RATSR: A Region Adaptive Transformer for Image Super-Resolution Based on Heat Feature Map

In an endeavor to develop a state-of-the-art model in the realm of image acceleration super-resolution (SR), we devised a sophisticated transformer-based classifier that accounts for regional features. Our innovative transformer, RATSR, seamlessly integrates features derived from the classifier into the SR process, while introducing a groundbreaking heatmap concept. This novel approach enables the image features to substantially enhance the attentional span of the super-resolution model, better utilizing regional information. Moreover, we conducted an in-depth exploration of the similarities and disparities between feature matching and feature fusion, optimizing components that are less perceptible to global information. Through rigorous theoretical analysis, we assessed the efficacy of our approach and proposed a comprehensive model architecture accompanied by a robust training strategy. Concurrently, we substantiated its effectiveness by employing extensive experimentation, and implemented ablation studies to fine-tune the model's modules. The culmination of our research effort is evidenced in the experimental results, which reveal that our cutting-edge image super-resolution model attains exceptional performance without necessitating a substantial increase in the number of parameters.