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Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from visible images would be highly valuable for the telemedicine community in order to show this medical information. To the best of our knowledge, there are limited works on visible-to-thermal (VT) face translation, and many current works go the opposite direction to generate visible faces from thermal surveillance images (TV) for law enforcement applications.

Plug-and-play (PnP) methods have recently emerged as a powerful
framework for image reconstruction that can flexibly combine different
physics-based observation models with data-driven image priors
in the form of denoisers, and achieve state-of-the-art image reconstruction
quality in many applications. In this paper, we aim to further
improve the computational efficacy of PnP methods by designing
a new algorithm that makes use of stochastic variance-reduced
gradients (SVRG), a nascent idea to accelerate runtime in stochastic

Camera pose estimation plays a crucial role in stitching overlapped images captured by a camera to achieve a broad view of interest. In this paper, we propose a robust camera pose estimation approach to stitching images of a large 3D surface with known geometry. In particular, given a collection of images, we first construct a relative pose matrix estimation of all image pairs from the collection, where each entry of the matrix is calculated by solving a perspective-n-point(PnP) problem over the corresponding pair of images.