Blind super-resolution using a learning-based approach

被引：38
|
作者：
Bégin, I [1 ]
Ferrie, FP [1 ]
机构：
[1] McGill Univ, Ctr Intelligent Machines, Montreal, PQ, Canada
来源：
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 | 2004年
关键词：
D O I：
10.1109/ICPR.2004.1334046
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The super-resolution of a single image of unknown point-spread-function (PSF) is addressed by extending a learning framework using blind deconvolution with an uncertainty around the resulting PSF. Results indicate success in refining the estimate of the PSF as well as to restoring the image. A novel disparity measure is also proposed to quantify the results.
引用
收藏
页码：85 / 89
页数：5
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