  Infrared small target super-resolution (SR) aims to recover reliable and
detailed high-resolution image with high-contrast targets from its
low-resolution counterparts. Since the infrared small target lacks color and
fine structure information, it is significant to exploit the supplementary
information among sequence images to enhance the target. In this paper, we
propose the first infrared small target SR method named local motion and
contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge
of infrared small target into deep network, which can mitigate the intrinsic
feature scarcity of infrared small targets. Specifically, motivated by the
local motion prior in the spatio-temporal dimension, we propose a local
spatio-temporal attention module to perform implicit frame alignment and
incorporate the local spatio-temporal information to enhance the local features
(especially for small targets). Motivated by the local contrast prior in the
spatial dimension, we propose a central difference residual group to
incorporate the central difference convolution into the feature extraction
backbone, which can achieve center-oriented gradient-aware feature extraction
to further improve the target contrast. Extensive experiments have demonstrated
that our method can recover accurate spatial dependency and improve the target
contrast. Comparative results show that MoCoPnet can outperform the
state-of-the-art video SR and single image SR methods in terms of both SR
performance and target enhancement. Based on the SR results, we further
investigate the influence of SR on infrared small target detection and the
experimental results demonstrate that MoCoPnet promotes the detection
performance. The code is available at https://github.com/XinyiYing/MoCoPnet.
