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

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Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

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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.

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Authors and Affiliations

  1. Institute of Biomedical Image Analysis, Private University for Health Sciences, Medical Informatics and Technology (UMIT), Eduard-Wallnöfer-Zentrum 1, 6060, Hall/Tyrol, Austria

    Martin Welk

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  1. Martin Welk
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Correspondence to Martin Welk .

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Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Welk, M. (2017). Robust Blind Deconvolution with Convolution-Spectrum-Based Kernel Regulariser and Poisson-Noise Data Terms. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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