Dual residual and large receptive field network for lightweight image super-resolution

Abstract

In recent years, there has been rapid progress in single-image super-resolution (SISR) reconstruction technology based on deep learning, but many methods face challenges in practical application due to their excessive computational requirements. To address this issue, numerous lightweight super-resolution (SR) methods have been proposed. Convolutional neural networks (CNNs) have demonstrated outstanding performance in lightweight SR reconstruction tasks. However, existing CNN-based lightweight SR networks still suffer from redundancy due to the use of small convolutional kernels and deeper network architectures for feature extraction. To solve these problems, we propose a novel network called Dual Residual and Large Receptive Field Network (DRLN), which includes two main designs. Firstly, a Large Receptive Field Feature Extraction Block (LRFEB) is proposed, which concatenates large kernel depth separable convolutions and large kernel depth separable dilated convolutions, effectively capturing spatial contextual information without increasing the number of parameters or computational burden, and enhancing the overall performance of the model. Secondly, in the Efficient Feature Distillation Block (EFDB), we innovatively propose a dual residual structure, which greatly improve the learning ability and feature expression richness of the network, and significantly improve the reconstruction performance. Extensive experiments demonstrate that DRLN surpasses BSRN which is the champion of the model complexity track of the NTIRE 2022 Efficient SR Challenge on all benchmark datasets, with fewer parameters and less computation.