局部运动与对比度先验驱动的红外小目标超分辨率深度网络
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution
AI总结：
提出首个红外小目标超分辨率方法MoCoPnet，通过局部时空注意力模块和中心差分残差组融合运动与对比度先验，有效提升了目标对比度、超分辨率及检测性能。
摘要：
红外小目标超分辨率（SR）旨在从低分辨率图像中恢复具有高对比度目标的可靠且细节丰富的高分辨率图像。由于红外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意义。本文提出首个红外小目标超分辨率方法，名为局部运动与对比度先验驱动的深度网络（MoCoPnet），将红外小目标的领域知识融入深度网络，以缓解红外小目标固有的特征稀缺问题。具体而言，受时空维度局部运动先验启发，我们提出局部时空注意力模块，执行隐式帧对齐并融合局部时空信息以增强局部特征（尤其是小目标）。受空间维度局部对比度先验启发，我们提出中心差分残差组，将中心差分卷积融入特征提取主干网络，实现面向中心的梯度感知特征提取，进一步提升目标对比度。大量实验表明，该方法能恢复准确的空间依赖关系并提升目标对比度。对比结果显示，MoCoPnet在SR性能和目标增强方面均优于当前最先进的视频SR和单图像SR方法。基于SR结果，我们进一步探究了SR对红外小目标检测的影响，实验结果表明MoCoPnet提升了检测性能。代码发布于https://github.com/XinyiYing/MoCoPnet。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
