Thermal imaging has numerous advantages over regular visible-range imaging since it performs well in low-light circumstances. Super-Resolution approaches can broaden their usefulness by replicating accurate high-resolution thermal pictures using measurements from low-cost, low-resolution thermal sensors. Because of the spectral range mismatch between the images, Guided Super-Resolution of thermal images utilizing visible range images is difficult. However, In case of failure to capture Visible Range Images can prevent the operations of applications in critical areas. We present a novel data fusion framework and regularization technique for Guided Super Resolution of Thermal images. The proposed architecture is computationally in-expensive and lightweight with the ability to maintain performance despite missing one of the modalities, i.e., high-resolution RGB image or the lower-resolution thermal image, and is designed to be robust in the presence of missing data. The proposed method presents a promising solution to the frequently occurring problem of missing modalities in a real-world scenario. Code is available at https://github.com/Kasliwal17/CoReFusion .

翻译：热成像在低光环境下表现优异，相较于常规可见光成像具有诸多优势。超分辨率技术可通过低成本、低分辨率热传感器的测量数据重建高精度热图像，从而拓展其应用范围。然而，由于可见光图像与热图像之间存在光谱范围差异，利用可见光图像引导热图像超分辨率存在一定难度。此外，可见光图像捕获失败可能导致关键领域应用无法运行。本文提出一种新颖的数据融合框架与正则化技术，用于热图像的引导式超分辨率。该架构计算成本低且轻量化，即便缺失高分辨率RGB图像或低分辨率热图像等单模态数据，仍能保持性能稳定，具备对缺失模态的鲁棒性。所提方法为实际场景中频繁出现的模态缺失问题提供了有效解决方案。代码开源地址：https://github.com/Kasliwal17/CoReFusion。