Multi-scale receptive field fusion network for lightweight image super-resolution

Abstract

In recent years, with the rapid development of deep learning, quantities of convolutional neural network (CNN) based methods are applied to the field of single image super-resolution (SISR), which have excellent performance compared with traditional ones. However, these methods have a large amount of calculation and memory consumption, limiting their application in edge devices. To address this problem, we propose a more lightweight multi-scale receptive field fusion network (MRFN) for fast and accurate SISR. Specifically, we first propose a multi-scale receptive field fusion block to fuse different scales of spatial information and enlarge the overall receptive field simultaneously. Secondly, based on the non-local sparse attention, we introduce a hash-learnable cheap non-local attention to capture long-range dependencies with much fewer parameters and calculations. Moreover, channel attention is also very important for SISR, we further combine directional second-order information and spatial coordinate information into it to enhance its capability, which is called second-order coordinate attention. The cooperation of these components leads to the success of our MRFN. Extensive experimental results show that our method achieves a better trade-off against other state-of-the-art methods in terms of SISR performance and model complexity.