Literature DB >> 17526360

Blind image deconvolution through support vector regression.

Dalong Li, Russell M Mersereau, Steven Simske.   

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.

Mesh:

Year:  2007        PMID: 17526360     DOI: 10.1109/TNN.2007.891622

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  2 in total

1.  A New Efficient Expression for the Conditional Expectation of the Blind Adaptive Deconvolution Problem Valid for the Entire Range ofSignal-to-Noise Ratio.

Authors:  Monika Pinchas
Journal:  Entropy (Basel)       Date:  2019-01-15       Impact factor: 2.524

2.  The Residual ISI for Which the Convolutional Noise Probability Density Function Associated with the Blind Adaptive Deconvolution Problem Turns Approximately Gaussian.

Authors:  Monika Pinchas
Journal:  Entropy (Basel)       Date:  2022-07-17       Impact factor: 2.738

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.