On missing data treatment for degraded video and film archives: A survey and a new Bayesian approach

被引:79
作者
Kokaram, AC [1 ]
机构
[1] Trinity Coll Dublin, Dept Elect Engn, Dublin, Ireland
关键词
autoregressive models; Bayesian inference; composition sampling; factored sampling; film and video post production; Gibbs sampling; image processing; marginalization; Markov chain Monte Carlo; missing data reconstruction; motion estimation; noise reduction; video processing; video restoration;
D O I
10.1109/TIP.2004.823815
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image sequence restoration has been steadily gaining in importance with the increasing prevalence of visual digital media. The demand for content increases the pressure on archives to automate their restoration activities for preservation of the cultural heritage that they hold. There are many defects that affect archived visual material and one central issue is that of Dirt and Sparkle, or "Blotches." Research in archive restoration has been conducted for more than a decade and this paper places that material in context to highlight the advances made during that time. The paper also presents a new and simpler Bayesian framework that achieves joint processing of noise, missing data, and occlusion.
引用
收藏
页码:395 / 413
页数:19
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