Superresolution Videos and Optical Flow based on Combinatorial and Variational Optimization

即使在图像质量非常差的情况下，人类也有能力从视频中提取非常精确的信息。这种惊人的能力只有在我们看视频的单个图像时才会显现出来，在那里我们发现它们通常是多么的嘈杂和模糊。在高清视频时代也是如此，虽然形式上具有很高的分辨率，但由于录制的自然限制，单帧无法提供与相同分辨率的照片相同的质量。在这个项目中，我们寻求将一个视频的连续帧的信息组合在一起，从而提高所有帧的质量和分辨率。作为中心假设，我们认为光流估计、去噪和超分辨率是耦合问题。因此，基于这些领域广泛的先前工作，我们将开发同时计算非常精确的光流和以更高分辨率去噪的单帧的技术。具体子项目涉及运动模糊、快速运动和闭塞在视频超分辨率背景下的建模。我们相信，光流和超分辨率的联合优化将为视频分析提供新的机会，它将使老电影的恢复成为可能，它将使现有的低分辨率视频提升到现代高清分辨率（甚至更高）。

Humans have the capability to draw very precise information from video even in case of very bad image quality. This astonishing capability becomes evident only when we look at the single images of a video, where we find out how noisy and blurry they typically are. This is also true in the era of HD videos, which formally have a high resolution, yet due to natural limitations in recording, single frames cannot provide the same quality as photos with the same resolution. In this project, the information of successive frames of a video are sought to be combined in a way that the quality and resolution of all frames can be increased. As the central hypothesis we claim that optical flow estimation, denoising, and superresolution are coupled problems. Thus, based on extensive prior work in these areas, we will develop techniques that simultaneously compute very precise optical flow and denoised single frames at a higher resolution. Concrete subprojects are concerned with the modeling of motion blur, fast motion, and occlusions in the context of video superresolution. We believe that joint optimization of optical flow and superresolution will provide new opportunities in video analysis, it will enable the restoration of old movies, and it will allow to lift existing low resolution videos to modern HD resolution (and beyond).