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Robust Blind Deconvolution with Convolution-Spectrum-Based Kernel Regulariser and Poisson-Noise Data Terms

  • Martin WelkEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

In recent work by Liu, Chang and Ma a variational blind deconvolution approach with alternating estimation of image and point-spread function was presented in which an innovative regulariser for the point-spread function was constructed using the convolution spectrum of the blurred image. Further work by Moser and Welk introduced robust data fidelity terms to this approach but did so at the cost of introducing a mismatch between the data fidelity terms used in image and point-spread function estimation. We propose an improved version of this robust model that avoids the mentioned inconsistency. We extend the model to multi-channel images and show experiments on synthetic and real-world images to compare the robust variants with the method by Liu, Chang and Ma.

Keywords

Blind Deconvolution Image Estimation Total Variation Regulariser Nonlinear Equation System Data Fidelity Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Biomedical Image AnalysisPrivate University for Health Sciences, Medical Informatics and Technology (UMIT)Hall/TyrolAustria

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