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Infrared small target super-resolution (SR) aims to recover reliable anddetailed high-resolution image with high-contrast targets from itslow-resolution counterparts. Since the infrared small target lacks color andfine structure information, it is significant to exploit the supplementaryinformation among sequence images to enhance the target. In this paper, wepropose the first infrared small target SR method named local motion andcontrast prior driven deep network (MoCoPnet) to integrate the domain knowledgeof infrared small target into deep network, which can mitigate the intrinsicfeature scarcity of infrared small targets. Specifically, motivated by thelocal motion prior in the spatio-temporal dimension, we propose a localspatio-temporal attention module to perform implicit frame alignment andincorporate the local spatio-temporal information to enhance the local features(especially for small targets). Motivated by the local contrast prior in thespatial dimension, we propose a central difference residual group toincorporate the central difference convolution into the feature extractionbackbone, which can achieve center-oriented gradient-aware feature extractionto further improve the target contrast. Extensive experiments have demonstratedthat our method can recover accurate spatial dependency and improve the targetcontrast. Comparative results show that MoCoPnet can outperform thestate-of-the-art video SR and single image SR methods in terms of both SRperformance and target enhancement. Based on the SR results, we furtherinvestigate the influence of SR on infrared small target detection and theexperimental results demonstrate that MoCoPnet promotes the detectionperformance. The code is available at https://github.com/XinyiYing/MoCoPnet.

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution