Regional Transformer for Image Super-Resolution

In the image super-resolution algorithm model, a large receptive field can provide more valuable features, so the Transformer with strong information interaction ability has achieved excellent results in image super-resolution processing applications. However, when the range of the receptive field reaches a certain critical value, the restoration performance of the super-resolution algorithm also reaches a certain critical value, which indicates that unconditionally increasing the receptive field will not continue to promote the improvement of the restoration performance. At the same time, the larger the receptive field range, the more data the model needs to process, which also seriously increases the computational complexity of the algorithm. In order to exchange information in a wider range more effectively, in this paper, a new type of super-resolution network based on Transformer, namely Regional Transformer, is designed. The key element in the newly designed network structure is the Region Block (RB) with the Boundary Restriction (BR) mechanism. In addition, the paper designs a Boundary Restriction based on coarse-to-fine pipes. This paper conducts a large number of experiments on multiple datasets, and the experiments show that the network structure designed in this paper has a significant improvement in performance.