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Denoising Multi-view Images Using Non-local Means with Different Similarity Measures

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Book cover Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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Abstract

We present a stereo image denoising algorithm. Our algorithm takes as an input a pair of noisy images of an object captured from two different directions (stereo images). We use either Maximum Difference or Singular Value Decomposition similarity metrics for identifying locations of similar searching windows in the input images. We adapt the Non-local Means algorithm for denoising collected patches from the searching windows. Experimental results show that our algorithm outperforms the original Non-local Means and our previous method Stereo images denoising using Non-local Means with Structural SIMilarity (S-SSIM), and it helps to estimate more accurate disparity maps at various noise levels.

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Acknowledgment

This research is partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). This support is greatly appreciated. This research is also partially funded by the Cultural Bureau of Saudi Arabia in Canada. This support is greatly appreciated.

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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 .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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© 2016 Springer International Publishing Switzerland

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Alkinani, M.H., El-Sakka, M.R. (2016). Denoising Multi-view Images Using Non-local Means with Different Similarity Measures. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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