A very lightweight and efficient image super-resolution network

Abstract

Deep convolutional neural networks significantly improve the performance of single image super-resolution (SISR). Generally, larger networks (i.e., deeper and wider) have better performance. However, larger networks require higher computing and storage costs, which limit their application on resource-constrained devices. Lightweight SISR networks with fewer parameters and smaller computational workloads are highly desirable. The key challenge is to obtain a better balance of model complexity and performance. In this paper, we propose a very lightweight and efficient SISR network. Our main contributions include: (1) Propose a frequency grouping fusion block (FGFB), which can better fuse high-/low-frequency feature information; (2) Propose a multi-way attention block (MWAB), which can exploit the multiple different clues of the feature information; (3) Propose a lightweight residual concatenation block (LRCB), which can combine the advantages of the residual connection and the channel concatenation; (4) Propose a lightweight convolutional block (LConv) for image super-resolution, which can significantly reduce the number of parameters; (5) Propose a progressive interactive group convolution (PIGC), which is more effective than the conventional group convolution. Extensive experimental results demonstrate that our method is significantly superior to other state-of-the-art methods currently available, with a better balance between model complexity and performance.