A lightweight image super-resolution network based on large receptive field information distillation

This paper tackles the problem of network structure redundancy in image super-resolution (SR) algorithms and aims to develop an efficient and low-parameter SR method. It proposes a lightweight SR network based on vast-receptive-field information distillation (VIDN), which enhances the model performance and reduces the parameter size by introducing large receptive field convolution and attention modules, optimizing convolution operations, and using simple gate functions instead of RELU activation functions. The network leverages large kernel convolution and multi-scale attention mechanisms to better capture global information and fuse local and global features of the image, respectively. The ESA and CCA modules complement each other to improve image clarity and realism, forming a powerful multi-scale attention mechanism. The experimental results on three scales and four benchmark datasets demonstrate that the proposed algorithm achieves an average PSNR improvement of 0.03db, an average parameter reduction of 53%, and clearer and more natural visual effects on images.