Blind Image Deconvolution Through Support Vector Regression

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 leve...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2007-05, Vol.18 (3), p.931-935
Hauptverfasser:	Li, D., Mersereau, R.M., Simske, S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Additive noise Algorithms Applied sciences Artificial Intelligence Blind deconvolution Blinds Computer science control theory systems Computer Simulation Connectionism. Neural networks Deconvolution Degradation Exact sciences and technology Image Interpretation, Computer-Assisted - methods Image restoration Information Storage and Retrieval - methods Iterative algorithms Laboratories Lucy-Richardson (LR) algorithm Mathematical analysis Maximum likelihood estimation Models, Theoretical Neural networks Neural Networks (Computer) Noise level Noise levels Noise robustness Pattern Recognition, Automated - methods peak signal-to-noise ratio (PSNR) PSNR Regression Regression Analysis support vector regression (SVR) Vectors (mathematics)
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Beschreibung
Zusammenfassung:	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
ISSN:	1045-9227 2162-237X 1941-0093 2162-2388
DOI:	10.1109/TNN.2007.891622