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A Unified Framework for the Restoration of Images Corrupted by Additive White Noise

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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

We propose a robust variational model for the restoration of images corrupted by blur and the general class of additive white noises. The solution of the non-trivial optimization problem, due to the non-smooth non-convex proposed model, is efficiently obtained by an Alternating Directions Method of Multipliers (ADMM), which in particular reduces the solution to a sequence of convex optimization sub-problems. Numerical results show the potentiality of the proposed model for restoring blurred images corrupted by several kinds of additive white noises.

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Acknowledgements

This work was supported by the “National Group for Scientific Computation (GNCS-INDAM)” and by ex60% project by the University of Bologna “Funds for selected research topics”.

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Authors and Affiliations

  1. Department of Mathematics, University of Bologna, Bologna, Italy

    Alessandro Lanza, Serena Morigi & Fiorella Sgallari

  2. Department of Mathematics, University of Genova, Genova, Italy

    Federica Sciacchitano

Authors
  1. Alessandro Lanza
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  2. Federica Sciacchitano
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  3. Serena Morigi
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  4. Fiorella Sgallari
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Corresponding author

Correspondence to Serena Morigi .

Editor information

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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Lanza, A., Sciacchitano, F., Morigi, S., Sgallari, F. (2017). A Unified Framework for the Restoration of Images Corrupted by Additive White Noise. 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_40

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

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