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Blur Detection via Phase Spectrum

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

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Abstract

The effectiveness of blur features is very important in blur detection from a single image and the most existing blur features are sensitive to the strong edges in the blurred image region which degrades the detection methods. We analyze the information carried by the reconstruction of an image from the phase spectrum alone (RIPS) and the influences of blurring on RIPS. We find that a clear image region has more intensity changes than a blurred one because the former has more high frequency components. And the local maxima of RIPS are at where these image components occur, which make the RIPS of the clear image regions are obviously bigger than that of the blurred ones. Based on this finding, we proposed a simple blur feature, called Phase Map (PM), generated by thresholding RIPS adaptively. And our blur detection method propagates PM to the final blur map only by filtering PM using the relative total variation (RTV) filter. Our proposed method is evaluated on challenging blur image datasets. The evaluation demonstrates that PM feature is effective for different blur types and our detection method performs better than the state-of-the-art algorithms quantitatively and qualitatively.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong, China

    Renyan Zhang

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  1. Renyan Zhang
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Correspondence to Renyan Zhang .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Zhang, R. (2019). Blur Detection via Phase Spectrum. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_46

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  • DOI: https://doi.org/10.1007/978-3-030-20876-9_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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