Title :
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
fDate :
5/1/2007 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.891622
