Exploiting self-similarities for single frame super-resolution

Chih Yuan Yang*, Jia Bin Huang, Ming Hsuan Yang

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

150 引文 斯高帕斯(Scopus)

摘要

We propose a super-resolution method that exploits self-similarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance.

原文英語
主出版物標題Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
頁面497-510
頁數14
版本PART 3
DOIs
出版狀態已出版 - 2011
對外發佈
事件10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, 新西蘭
持續時間: 08 11 201012 11 2010

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 3
6494 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference10th Asian Conference on Computer Vision, ACCV 2010
國家/地區新西蘭
城市Queenstown
期間08/11/1012/11/10

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