Super-Resolution Reconstruction of Infrared Images With Edge-Enhanced and Variable Activation Network

Abstract

Infrared images have less available information compared to visible images, and the applying of high-frequency details and edge information can directly influence the quality of super-resolution (SR) reconstruction of infrared images. However, most existing SR methods have a single activation mode for high-frequency features and over-dependently increase the network depth to improve performance. To address these problems, we design a variable GELU (VGELU), which introduces a learnable parameter a based on GELU to suppress low-frequency features and noise by adaptively changing the slope of GELU in high-frequency feature extraction. In addition, we propose an attention-enhanced CATS-RCF (ACR) network in the strong edge feature extraction module (SEFEM), which introduces coordinate attention based on CATS-RCF (CR) to enhance the edge weights of infrared low-resolution (LR) images and improve the effect of edge extraction. To fully fuse high-frequency features and edge information, we further design an edge feature fusion block (EFFB), which effectively fuses edge information from different dimensions. Our edge-enhanced and variable activation network (EVAN) is constructed by applying the proposed VGELU, SEFEM with EFFB. The comprehensive experiments demonstrate the superiority of our EVAN over other comparison methods.