Regularized motion blur-kernel estimation with adaptive sparse image prior learning

作者：

Highlights：

• A new motion blur-kernel estimation method is proposed for blind image deblurring.

• The new method is formulated in a unified and rigorous optimization perspective.

• Sparse image priors are learned adaptively for each blind deblurring problem.

• The noise variance is automatically estimated unlike state-of-the art VB methods.

• The method achieves better performance in terms of deblurring effectiveness

摘要

•A new motion blur-kernel estimation method is proposed for blind image deblurring.•The new method is formulated in a unified and rigorous optimization perspective.•Sparse image priors are learned adaptively for each blind deblurring problem.•The noise variance is automatically estimated unlike state-of-the art VB methods.•The method achieves better performance in terms of deblurring effectiveness

论文关键词：Blind deconvolution,Motion deblurring,Hierarchical prior,Jeffreys,Variational Bayes,Iteratively reweighted least squares

论文评审过程：Received 9 August 2014, Revised 22 August 2015, Accepted 29 September 2015, Available online 9 October 2015, Version of Record 27 November 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.034