Since thermal imagery offers a unique modality to investigate pain, the U.S. National Institutes of Health (NIH) has collected a large and diverse set of cancer patient facial thermograms for AI-based pain research. However, differing angles from camera capture between thermal and visible sensors has led to misalignment between Visible-Thermal (VT) images. We modernize the classic computer vision task of image registration by applying and modifying a generative alignment algorithm to register VT cancer faces, without the need for a reference or alignment parameters. By registering VT faces, we demonstrate that the quality of thermal images produced in the generative AI downstream task of Visible-to-Thermal (V2T) image translation significantly improves up to 52.5\%, than without registration. Images in this paper have been approved by the NIH NCI for public dissemination.

翻译：由于热成像为疼痛研究提供了独特模态，美国国立卫生研究院（NIH）已采集了大量多样化的癌症患者面部热像图，用于基于人工智能（AI）的疼痛研究。然而，热成像传感器与可见光传感器在相机拍摄角度上的差异，导致可见光-热成像图像之间出现未对准现象。我们通过应用并改进一种生成式对齐算法，对经典的计算机视觉图像配准任务进行了现代化改造，以配准可见光-热成像癌症面部图像，无需参考图像或对齐参数。通过配准可见光-热成像面部图像，我们证明在生成式AI的下游任务——可见光到热成像图像转换中，热成像图像的质量比未经配准时显著提升，最高提升达52.5%。本文中的图像已获NIH国家癌症研究所批准公开传播。