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

页码：85 / 89

页数：5

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