InfraFFN: A Feature Fusion Network leveraging dual-path convolution and self-attention for infrared image super-resolution

Feiwei Qin, Zhengwei Shen, Ruiquan Ge, Kai Zhang, Fei Lin, Yeru Wang, Juan Manuel Górriz, Ahmed Elazab, Changmiao Wang

Abstract: Image super-resolution (SR) is a classic visual problem that aims to generate high-quality, high-resolution images from low-resolution inputs. However, most deep learning methods are designed for visible images and often overlook infrared images, which play a crucial role in numerous research fields such as aerospace and remote sensing. Due to hardware limitations, infrared images possess a lower resolution and exhibit characteristics distinct from visible images, including low contrast and indistinct gradients. These unique patterns are challenging to extract and represent. To address this issue, we propose a Infrared Feature Fusion Network (InfraFFN) for infrared image SR in this paper. Specifically, we design a Residual Feature Fusion Block (RFFB) for deep feature extraction. Each Feature Fusion Block (FFB) within RFFB effectively combines the advantages of convolution and self-attention, and utilizes bi-directional information interactions across branches to better model in both channel and spatial dimensions. Furthermore, considering the low contrast in infrared images, we designed a dual-path convolution structure to extract features under different sizes of receptive fields and fuse features at various scales. Extensive experiments demonstrate that our InfraFFN achieves superior visual improvement on multiple infrared image datasets compared to state-of-the-art methods. The source codes are available at https://github.com/szw811/InfraFFN.