Robust super-resolution

被引：0

作者：

Zomet, A ^{[1
]}

Rav-Acha, A ^{[1
]}

Peleg, S ^{[1
]}

机构：

[1] Hebrew Univ Jerusalem, Sch Comp Sci & Engn, IL-91904 Jerusalem, Israel

来源：

2001 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS | 2001年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion err-os, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super. resolution methods actually degrade the image.

引用

页码：645 / 650

页数：6

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