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Natural Images Enhancement Using Structure Extraction and Retinex

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Book cover Advanced Concepts for Intelligent Vision Systems (ACIVS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12002))

Abstract

Variational Retinex model-based methods for low-light image enhancement have been popularly studied in recent years. In this paper, we present an enhanced variational Retinex method for low-light natural image enhancement, based on the initial smoother illumination component with a structure extraction technique. The Bergman splitting algorithm is then introduced to estimate the illuminance component and reflectance component. The de-block processing and illuminance component correction are used for the enhanced reflectance as the ultimate enhanced image. Moreover, the estimated smoother illumination component can make enhanced images preserve edge details. Experimental results with a comparison demonstrate the present variational Retinex method can effectively enhance image quality and maintain image color.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Xiaoyu Du & Youshen Xia

Authors
  1. Xiaoyu Du
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  2. Youshen Xia
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Corresponding author

Correspondence to Youshen Xia .

Editor information

Editors and Affiliations

  1. DGA, Paris, France

    Jacques Blanc-Talon

  2. University of Auckland, Auckland, New Zealand

    Patrice Delmas

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. CSIRO, Canberra, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Du, X., Xia, Y. (2020). Natural Images Enhancement Using Structure Extraction and Retinex. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_35

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  • DOI: https://doi.org/10.1007/978-3-030-40605-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40604-2

  • Online ISBN: 978-3-030-40605-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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