Hierarchical Perceptual Distillation Network for Lightweight Image Super-Resolution Reconstruction

Abstract

Recently, the image super-resolution (SR) has made remarkable progress. However, due to the proliferation of resource-constrained scenarios, the computational-intensive SR technology is limited in portable devices. Therefore, high efficiency and lightweight become the key factors of image SR in the real world. To overcome these problems, we propose a hierarchical perceptual distillation network (HPDN), which is a lightweight solution that includes two efficient designs. Firstly, we construct the context cooperation perception attention (CCPA), which adds rich information structure by introducing various pooling modes to obtain more accurate content. Secondly, the proposed multi-scale Dconv sparse attention (MSDSA) captures input features at multiple scales, and pays attention to global information at different receptive fields for image reconstruction. A large number of experiments show that our network almost achieves the best results compared with the SOTA methods.