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Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection

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Scale Space and Variational Methods in Computer Vision (SSVM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14009))

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Abstract

We propose a two-stage variational model for the additive decomposition of images into piecewise constant, smooth, textured and white noise components. The challenging separation of noise from texture is successfully achieved by including a normalized whiteness constraint in the model, and the selection of the regularization parameters is performed based on a novel multi-parameter cross-correlation principle. The two resulting minimization problems are efficiently solved by means of the alternating directions method of multipliers. Numerical results show the potentiality of the proposed model for the decomposition of textured images corrupted by several kinds of additive white noises.

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Acknowledgments

This work was supported in part by the National Group for Scientific Computation (GNCS-INDAM) and in part by MUR RFO projects.

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Authors and Affiliations

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

    Laura Girometti, Martin Huska, Alessandro Lanza & Serena Morigi

Authors
  1. Laura Girometti
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  2. Martin Huska
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  3. Alessandro Lanza
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  4. Serena Morigi
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Corresponding author

Correspondence to Alessandro Lanza .

Editor information

Editors and Affiliations

  1. CNRS, Université Côte d'Azur, Sophia-Antipolis, France

    Luca Calatroni

  2. University of Insubria, Como, Italy

    Marco Donatelli

  3. University of Bologna, Bologna, Italy

    Serena Morigi

  4. University of Modena and Reggio Emilia, Modena, Italy

    Marco Prato

  5. University of Genova, Genova, Italy

    Matteo Santacesaria

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Girometti, L., Huska, M., Lanza, A., Morigi, S. (2023). Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-31975-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31974-7

  • Online ISBN: 978-3-031-31975-4

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