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International Conference Image Analysis and Recognition

ICIAR 2011: Image Analysis and Recognition pp 1–10Cite as

Enhancing Video Denoising Algorithms by Fusion from Multiple Views

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

Abstract

Video denoising is highly desirable in many real world applications. It can enhance the perceived quality of video signals, and can also help improve the performance of subsequent processes such as compression, segmentation, and object recognition. In this paper, we propose a method to enhance existing video denoising algorithms by denoising a video signal from multiple views (front-, top-, and side-views). A fusion scheme is then proposed to optimally combine the denoised videos from multiple views into one. We show that such a conceptually simple and easy-to-use strategy, which we call multiple view fusion (MVF), leads to a computationally efficient algorithm that can significantly improve video denoising results upon state-of-the-art algorithms. The effect is especially strong at high noise levels, where the gain over the best video denoising results reported in the literature, can be as high as 2-3 dB in PSNR. Significant visual quality enhancement is also observed and evidenced by improvement in terms of SSIM evaluations.

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Author information

Authors and Affiliations

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

    Kai Zeng & Zhou Wang

Authors
  1. Kai Zeng
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  2. Zhou Wang
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, 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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© 2011 Springer-Verlag Berlin Heidelberg

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Zeng, K., Wang, Z. (2011). Enhancing Video Denoising Algorithms by Fusion from Multiple Views. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-21593-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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