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The Use of Residuals in Image Denoising

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Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

State-of-the-art image denoising algorithms attempt to recover natural image signals from their noisy observations, such that the statistics of the denoised image follow the statistical regularities of natural images. One aspect generally missing in these approaches is that the properties of the residual image (defined as the difference between the noisy observation and the denoised image) have not been well exploited. Here we demonstrate the usefulness of residual images in image denoising. In particular, we show that well-known full-reference image quality measures such as the mean-squared-error and the structural similarity index can be estimated from the residual image without the reference image. We also propose a procedure that has the potential to enhance the image quality of given image denoising algorithms.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematics, Canada

    Dominique Brunet & Edward R. Vrscay

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Zhou Wang

Authors
  1. Dominique Brunet
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  2. Edward R. Vrscay
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  3. Zhou Wang
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2009 Springer-Verlag Berlin Heidelberg

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Brunet, D., Vrscay, E.R., Wang, Z. (2009). The Use of Residuals in Image Denoising. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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