Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation problems. In this paper, we propose a solution to the joint problem of image super-resolution and multi-modality image-to-image translation. The problem can be stated as the recovery of a high-resolution image in a modality, given a low-resolution observation of the same image in an alternative modality. Our paper offers two models to address this problem and will be evaluated on the recovery of high-resolution day images given low-resolution night images of the same scene. Promising qualitative and quantitative results will be presented for each model.
翻译:过去几年来,以深层次学习为基础的技术,如 " 创造反向网络 " (GANs),大大改进了图像超分辨率和图像到图像翻译问题的解决办法,在本文件中,我们提出了解决图像超分辨率和多式图像到图像翻译等共同问题的办法,问题可以称为在一种模式中恢复高分辨率图像,在另一种模式中低分辨率观测同一图像。我们的文件为解决这一问题提供了两种模式,并将评估在同一场景中以低分辨率夜视图像为高分辨率日图像的恢复情况。