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Non-local Means for Stereo Image Denoising Using Structural Similarity

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Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

We present a novel stereo image denoising algorithm. Our algorithm takes as an input a pair of noisy images of an object captured form two different directions. We use the structural similarity index as a similarity metric for identifying locations of similar patches in the input images. We adapt the Non-Local Means algorithm for denoising collected patches from the input images. We validate our algorithm on various stereo images at various noise levels. Experimental results show that the denoising performance of our algorithm is better than the original Non-Local Means and Stereo-MSE methods at low noise level \(\left( \sigma \leqslant 20\right) \).

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Western Ontario, London, ON, N6A 5B7, Canada

    Monagi H. Alkinani & Mahmoud R. El-Sakka

Authors
  1. Monagi H. Alkinani
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  2. Mahmoud R. El-Sakka
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Corresponding author

Correspondence to Mahmoud R. El-Sakka .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Alkinani, M.H., El-Sakka, M.R. (2015). Non-local Means for Stereo Image Denoising Using Structural Similarity. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

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