PCCFormer: Parallel coupled convolutional transformer for image super-resolution

In recent years, with the continuous development of deep learning technology, image super-resolution (SR) has witnessed tremendous progress. Transformer as an emerging deep learning model, its application in the field of image SR is also gradually attracted the attention of researchers. In this paper, we propose parallel coupled convolutional transformer (PCCFormer) architecture for image SR. The proposed design uses transformer and convolutional neural network (CNN) as the structure of the network, thus leveraging the complementary benefits. We use parallel attention transformer to improve feature expression ability of the model. We introduce adaptive convolution residual block to effectively extract the image local information. We introduce a novel concurrent module to aggregate enriched features, including long-range features from transformer and local features captured by CNN. Extensive experiments conducted on benchmark datasets illustrate that PCCFormer surpasses existing image SR methods. For example, our PCCFormer achieves the PSNR of 34.01 dB on Urban100 dataset with scale factor 2.