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Modern-day surveillance systems perform person recognition using deeplearning-based face verification networks. Most state-of-the-art facialverification systems are trained using visible spectrum images. But, acquiringimages in the visible spectrum is impractical in scenarios of low-light andnighttime conditions, and often images are captured in an alternate domain suchas the thermal infrared domain. Facial verification in thermal images is oftenperformed after retrieving the corresponding visible domain images. This is awell-established problem often known as the Thermal-to-Visible (T2V) imagetranslation. In this paper, we propose a Denoising Diffusion ProbabilisticModel (DDPM) based solution for T2V translation specifically for facial images.During training, the model learns the conditional distribution of visiblefacial images given their corresponding thermal image through the diffusionprocess. During inference, the visible domain image is obtained by startingfrom Gaussian noise and performing denoising repeatedly. The existing inferenceprocess for DDPMs is stochastic and time-consuming. Hence, we propose a novelinference strategy for speeding up the inference time of DDPMs, specificallyfor the problem of T2V image translation. We achieve the state-of-the-artresults on multiple datasets. The code and pretrained models are publicallyavailable at http://github.com/Nithin-GK/T2V-DDPM

T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models