文章基本信息

标题：Total Variation 正則化手法と事例学習法を組合せた超解像度画像の復元法
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作者：桜井優 ; 吉川明博 ; 鈴木彰太郎等
期刊名称：映像情報メディア学会誌
印刷版ISSN：1342-6907
电子版ISSN：1881-6908
出版年度：2010
卷号：64
期号：11
页码：1613-1620
DOI：10.3169/itej.64.1613
出版社：The Institute of Image Information and Television Engineers
摘要："Super-resolution" is not only a key word with its own active research area but is also used in sales messages for new consumer products such as HDTV. Of the many proposals for super-resolution image reconstruction, the total variation (TV) regularization method seems to be the most successful approach due to its sharp edge preservation and no artifacts. The TV regularization method still has two problems. One is the large computational time, and the other is insufficient texture interpolation. In this paper, we propose a system that solves these problems. In our system, the number of TV regularization processes is smaller than that of the conventional method, and the learning-based method is introduced in place of texture interpolation. The learning-based method is another super-resolution approach. This paper proposes combining the TV regularization and learning-based methods. The experimental results show that our approach performs well and reduces computational time while being robustness to the input noise.
关键词：超解像;Total Variation正則化;事例学習法;画像拡大