PET Center, Rigshospitalet (Copenhagen University Hospital), Copenhagen, Denmark.
IEEE Trans Image Process. 2011 Jul;20(7):1870-84. doi: 10.1109/TIP.2011.2106793. Epub 2011 Jan 17.
In this paper, we propose an energy-based algorithm for motion-compensated video super-resolution (VSR) targeted on upscaling of standard definition (SD) video to high-definition (HD) video. Since the motion (flow field) of the image sequence is generally unknown, we introduce a formulation for the joint estimation of a super-resolution (SR) sequence and its flow field. Via the calculus of variations, this leads to a coupled system of partial differential equations for image sequence and motion estimation. We solve a simplified form of this system and, as a by-product, we indeed provide a motion field for super-resolved sequences. To the best of our knowledge, computing super-resolved flows has not been done before. Most advanced SR methods found in literature cannot be applied to general video with arbitrary scene content and/or arbitrary optical flows, as it is possible with our simultaneous VSR method. A series of experiments shows that our method outperforms other VSR methods when dealing with general video input and that it continues to provide good results even for large scaling factors up to 8 × 8.
本文提出了一种基于能量的运动补偿视频超分辨率（VSR）算法，旨在将标准定义（SD）视频上转换为高定义（HD）视频。由于图像序列的运动（流场）通常是未知的，我们引入了一种用于联合估计超分辨率（SR）序列及其流场的公式。通过变分法，这导致了一个用于图像序列和运动估计的耦合偏微分方程组。我们解决了这个系统的一个简化形式，并且作为副产品，我们确实为超分辨率序列提供了一个运动场。据我们所知，以前没有计算过超分辨率流。文献中发现的大多数先进的 SR 方法都不能应用于具有任意场景内容和/或任意光流的一般视频，而我们的同时 VSR 方法则可以。一系列实验表明，我们的方法在处理一般视频输入时优于其他 VSR 方法，并且即使对于高达 8×8 的大放大因子，它也能继续提供良好的结果。
