Information Scaling Distillation Network for Lightweight Single Image Super-Resolution

Recently, the lightweight single image super-resolution (SISR) model based on information distillation has attracted the attention of many researchers due to its ability to recover high-resolution images quickly. We reassess and delve into the advantages and disadvantages of information distillation structures, and propose an information scaling distillation network (ISDN) for lightweight single image super-resolution, which can accurately and efficiently restore high-resolution images. By optimizing the distillation branch and feature branch of the information distillation, we meticulously designed the stacked block deep scaling distillation block (DSDB) to enlarge the receptive field and increase the network depth. We mainly optimize and design from two aspects. Firstly, we extract redundant information in the distillation branch and integrate it into multiple layers to form deep information transmission. Secondly, we design a blueprint deep scaling residual (BDSR) in the feature branch, which can extract advanced semantic image information, compress and expand feature channels. The qualitative and quantitative results on various benchmark datasets demonstrate the advantages of our model in terms of model parameters, multiply-accumulate operations, test efficiency, and image reconstruction quality. Code is available at https://github.com/ycLi-CV/ISDN-main.