Single infrared image super-resolution based on lightweight multi-path feature fusion network

Single infrared (IR) image super-resolution methods can help to reduce the cost and difficulty in manufacturing IR sensors for the imaging system. However, the deep learning-based image SR methods need to build a complex network and thus consume a lot of computational power, which limits the application of SR technology on devices with low computing resources in practice. To solve this problem, the authors present a lightweight multi-path feature fusion network (MFFN) for the single infrared (IR) image SR. A multi-path feature fusion block (MFFB) is developed to extract and fuse multiple and discriminative features in a recursive feedback way. Specifically, the multiple features are refined via the linear feature extraction branch, shared-source residual feature extraction branch, and channel attention branch in MFFB. Finally, the authors reconstruct the high-resolution IR images from the low-resolution counterpart based on the refined multiple features. The experimental results demonstrate that MFFN achieves high-quality single infrared image SR and shows superiority over previous methods for several scale factors (e.g. x2, x3, and x4). MFFN has potential applications in the mobile infrared imaging system.