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.

翻译：红外弱小目标超分辨率旨在从低分辨率图像中恢复细节丰富、目标对比度高且可靠的高分辨率图像。由于红外弱小目标缺乏颜色和精细结构信息，利用序列图像间的补充信息来增强目标具有重要研究价值。本文提出首个面向红外弱小目标的超分辨率方法——局部运动与对比度先验驱动的深度网络（MoCoPnet），将红外弱小目标的领域知识融入深度网络以缓解其固有的特征稀缺问题。具体而言，受时空维度局部运动先验的启发，我们提出局部时空注意力模块实现隐式帧对齐，并通过融合局部时空信息增强局部特征（特别是小目标）。受空间维度局部对比度先验的启发，我们提出中心差分残差组，将中心差分卷积引入特征提取主干网络，实现中心导向的梯度感知特征提取以进一步提升目标对比度。大量实验表明，该方法能准确恢复空间依赖性并提升目标对比度。对比结果显示，MoCoPnet在超分辨率性能和目标增强方面均优于当前最优的视频超分辨率及单帧图像超分辨率方法。基于超分辨率结果，我们进一步探讨了超分辨率对红外弱小目标检测的影响，实验证明MoCoPnet能促进检测性能提升。代码已开源至https://github.com/XinyiYing/MoCoPnet。