FADLSR: A Lightweight Super-Resolution Network Based on Feature Asymmetric Distillation

Super-resolution (SR) technology based on deep learning has achieved excellent results. However, too many convolution layers and parameters consume a very high computational cost and storage space when training the model, which dramatically limits the practical application. To solve this problem, this paper proposes a lightweight feature asymmetric distillation SR network (FADLSR). FADLSR constructs the feature extractor module through the stacked feature asymmetric distillation block (FADB). It extracts the low-resolution image features hierarchically and integrates them to obtain more representative features to improve the SR quality. In addition, we design a new focus block and add it to FADB to improve the quality of feature acquisition. We also introduce asymmetric convolution to strengthen the key features of the skeleton region. Detailed experiments show that our FADLSR has achieved excellent results in objective evaluation criteria and subjective visual effect on the test sets of Set5, Set14, B100, Urban100, and Manga109. The parameters of our model are roughly the same as those of the current state-of-the-art models. Moreover, in terms of model performance, FADLSR is 10–15% higher than the comparison algorithms.