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

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 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 a based on CATS-RCF to enhance the edge weights of infrared low-resolution 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.