论文标题
使用生成对抗网络的多模式图像超分辨率
Multi-Modality Image Super-Resolution using Generative Adversarial Networks
论文作者
论文摘要
在过去的几年中，基于学习的技术（例如生成对抗网络（GAN））具有显着改进的解决方案，以形象超分辨率和图像对图像翻译问题。在本文中，我们提出了解决图像超分辨率和多模式图像到图像转换的联合问题的解决方案。如果在替代方式中对同一图像的低分辨率观察，则该问题可以说为模式中高分辨率图像的恢复。我们的论文提供了两个模型来解决此问题，并将在同一场景的低分辨率夜间图像中对高分辨率日图像的恢复进行评估。每个模型都会提出有希望的定性和定量结果。
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.
