Infrared Thermal Imaging Super-Resolution via Multiscale Spatio-Temporal Feature Fusion Network

Infrared thermal imaging is widely used in military and civil fields like security and protecting monitoring, military reconnaissance, road traffic monitoring. However, most current infrared sensors have limited resolution for their technological limitation. To solve it, we propose a novel end-to-end infrared thermal imaging super-resolution via multiscale spatio-temporal feature fusion network, that can fuse the temporal aligned features of the neighboring frames and the spatial feature of the reference frame. In our network, a pyramid multiscale fusion module is designed as major kernel of the whole structure, which could release the spatio-temporal misalignment between the neighboring frames and the reference frame. Specifically, we introduce the channel attention mechanism to further improve the infrared thermal imaging video super-resolution performance. Qualitative and quantitative evaluation results prove the effectiveness of our framework on both Vid4 dataset and raw infrared dataset collected by ourselves. We provide the infrared thermal imaging super-resolution results at https://github.com/qq123-jpg/Infrared-Thermal-Imaging-Super-resolution-via-Multiscale-Spatio-temporal-Feature-Fusion-Network.