An Algorithm for Total Variation Minimization and Applications
An Algorithm for Total Variation Minimization and Applications
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DOI：
10.1023/b:jmiv.0000011325.36760.1e
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发表时间：
2004
影响因子：
2
通讯作者：
A. Chambolle
中科院分区：
文献类型：
--
作者：
A. Chambolle
最近，存档序列的自动恢复问题引起了视频广播行业的注意。其中一个主要问题是由于薄膜磨损或污垢附着而造成的斑点。本文提出了一种新的框架，同时处理丢失的数据和运动在退化的视频序列。使用简单的，平移的运动模型，联合解决方案的检测，并重建丢失的数据。该框架还结合了独特的概念，处理遮挡和揭露，因为它涉及到图片建设。我们的想法是使用MCMC来解决贝叶斯框架下的问题，但部署纯粹的确定性机制来处理解决方案。这导致相对快速的实现，其统一了文献中先前描述的许多逐像素方案。
Recently, the problem of automated restoration of archived sequences has caught the attention of the Video Broadcast industry. One of the main problems is deadling with Blotches caused by film abrasion or dirt adhesion. This paper presents a new framework for the simultaneous treatment of missing data and motion in degraded video sequences. Using simple, translational models of motion, a joint solution for the detection, and reconstruction of missing data is proposed. The framework also incorporates the unique notion of dealing with occlusion and uncovering as it pertains to picture building. The idea is to use MCMC to solve the resulting problem articulated under a Bayesian framework, but to deploy purely deterministic mechanisms for dealing with the solution. This results in a relatively fast implementation that unifies many of the pixel-by-pixel schemes previously described in the literature.