Lightweight single image super-resolution based on multi-path progressive feature fusion and attention mechanism

Single image super-resolution (SISR) is a fundamental image processing task, which aims to generate a high-resolution (HR) image from its low-resolution (LR) counterpart. Deep convolution neural networks (CNNs) have significantly improved the performance of SISR, and dominated the current research on SISR techniques. However, the performance improvement depends heavily on the size of the networks. In general, the deeper the networks, the better the performance, which limits their use to devices with limited computing and memory resources. The challenge is to find the optimal balance between network model complexity and SISR performance. In this paper, we propose a new lightweight SISR algorithm based on CNNs, which uses multi-path progressive feature fusion and attention mechanism. Our main contributions are as follows: (1) We propose a multi-path progressive feature fusion block (PFF), which can use the feature from the previous path to gradually guide the feature learning of the next path step by step in multiple paths. (2) We propose a multi-path feature attention mechanism (FAM), which can adaptively weigh the multi-path feature channels to be concatenated, improve the utilization of feature information and feature representation capability. The experimental results show that whether it is an objective measurement or a subjective measurement, our method is better than other similar state of the art methods, and has a better model complexity and performance balance.