复杂场景的视频修复

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摘要翻译：
我们提出了一种基于全局的、基于补丁的函数优化的视频自动修复算法。该算法能够处理视频修复过程中自然出现的各种具有挑战性的情况，如动态纹理的正确重建、多个运动物体的正确重建和运动背景的正确重建。此外，我们实现这一点的执行时间比最先进的执行时间少了一个数量级。我们也能够在高清视频上取得良好的质量效果。最后，我们给出了具体的算法细节，以使算法的实现尽可能容易。所得到的算法不需要分割或手工输入，除了定义修复掩码之外，可以处理比以往工作更广泛的各种情况。1.导言。先进的图像和视频编辑技术在图像处理和计算机视觉领域越来越普遍，也开始应用于媒体娱乐领域。与视频编辑密切相关的一个常见而困难的任务是图像和视频的“修复”。一般说来，这是一个任务，以一些其他的内容取代图像或视频的内容，这是视觉上赏心悦目。这一主题在图像的情况下已经得到了广泛的研究，以至于面向普通公众的商业图像修复产品已经可用，如Photoshop的“内容感知填充”[1]。然而，虽然在视频中取得了一些令人印象深刻的结果，但这一主题的研究远不如图像修复广泛。这种相对缺乏的研究可以很大程度上归因于由于增加的时间维度而导致的高时间复杂性。事实上，直到最近才有可能在高清晰度视频上产生高质量的修复结果，而且这只是以半自动的方式进行。然而，高质量的视频修复有许多重要而有用的应用，如电影修复、影院专业后期制作和个人使用的视频编辑。出于这个原因，我们相信一个自动的，通用的视频修复算法将是非常有用的学术和专业社区。
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英文标题：
《Video Inpainting of Complex Scenes》
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作者：
Alasdair Newson, Andr\'es Almansa (LTCI), Matthieu Fradet, Yann
  Gousseau, Patrick P\'erez
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最新提交年份：
2015
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multimedia        多媒体
分类描述：Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Mathematics        数学
二级分类：Numerical Analysis        数值分析
分类描述：Numerical algorithms for problems in analysis and algebra, scientific computation
分析和代数问题的数值算法，科学计算
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英文摘要：
  We propose an automatic video inpainting algorithm which relies on the optimisation of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask, and can deal with a wider variety of situations than is handled by previous work. 1. Introduction. Advanced image and video editing techniques are increasingly common in the image processing and computer vision world, and are also starting to be used in media entertainment. One common and difficult task closely linked to the world of video editing is image and video " inpainting ". Generally speaking, this is the task of replacing the content of an image or video with some other content which is visually pleasing. This subject has been extensively studied in the case of images, to such an extent that commercial image inpainting products destined for the general public are available, such as Photoshop's " Content Aware fill " [1]. However, while some impressive results have been obtained in the case of videos, the subject has been studied far less extensively than image inpainting. This relative lack of research can largely be attributed to high time complexity due to the added temporal dimension. Indeed, it has only very recently become possible to produce good quality inpainting results on high definition videos, and this only in a semi-automatic manner. Nevertheless, high-quality video inpainting has many important and useful applications such as film restoration, professional post-production in cinema and video editing for personal use. For this reason, we believe that an automatic, generic video inpainting algorithm would be extremely useful for both academic and professional communities.
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PDF链接：
https://arxiv.org/pdf/1503.05528

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