论文标题
T2V-DDPM：使用denoising扩散概率模型的热到可见的面部翻译
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
论文作者
论文摘要
现代监视系统使用基于深度学习的面部验证网络执行人员认可。大多数最先进的面部验证系统都是使用可见光谱图像训练的。但是，在弱光和夜间条件的情况下，在可见光谱中获取图像是不切实际的，并且经常在替代域中捕获图像，例如热红外域。在检索相应的可见域图像后，通常在热图像中进行面部验证。这是一个良好的问题，通常称为热能（T2V）图像翻译。在本文中，我们建议针对面部图像的T2V翻译基于Denoising扩散概率模型（DDPM）解决方案。在训练过程中，该模型通过扩散过程了解了它们相应的热图像，可见面部图像的条件分布。在推断过程中，可见的域图像是通过从高斯噪声开始并反复执行的。 DDPM的现有推理过程是随机且耗时的。因此，我们提出了一种新颖的推理策略，以加快DDPM的推理时间，特别是用于T2V图像翻译问题。我们在多个数据集上实现了最新结果。代码和验证的模型可在http://github.com/nithin-gk/t2v-ddpm上公开获得
Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPM
