Blind Image Deconvolution Through Support Vector Regression

Author

Li, D. ; Mersereau, R.M. ; Simske, S.

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA

Volume

18

Issue

3

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

931

Lastpage

935

Abstract

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

Keywords

image restoration; regression analysis; support vector machines; blind image deconvolution; noisy blurred image restoration; point spread function; support vector regression; Additive noise; Deconvolution; Degradation; Image restoration; Iterative algorithms; Laboratories; Maximum likelihood estimation; Noise level; Noise robustness; PSNR; Blind deconvolution; Lucy–Richardson (LR) algorithm; peak signal-to-noise ratio (PSNR); support vector regression (SVR); Algorithms; Artificial Intelligence; Computer Simulation; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated; Regression Analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.891622

Filename

4182392