Blind image deconvolution through support vector regression

被引：59

作者：

Li, Dalong ^{[1
]}

Mersereau, Russell M. ^{[1
]}

Simske, Steven ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Ctr Signal & Image Proc, Atlanta, GA 30332 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 03期

关键词：

blind deconvolution; Lucy-Richardson (LR) algorithm; peak signal-to-noise ratio (PSNR); support vector regression (SVR);

D O I：

10.1109/TNN.2007.891622

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This letter introduces a new algorithm for the restoration of a noisy blurred image based on the support vector regression (SVR). Experiments show that the performance of the SVR is very robust in blind image deconvolution where the types of blurs, point spread function (PSF) support, and noise level are all unknown.

引用

页码：931 / 935

页数：5

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