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Retinex Based Flicker-Free Low-Light Video Enhancement

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

Videos captured in low light environment tend to be poor visual effect. To get better visual experience, a video enhancement algorithm based on improved center-surrounded Retinex and optical flow is proposed in this paper, which contains intra-frame brightness enhancement and inter-frame brightness continuity. In intra-frame brightness enhancement, reflection of each frame is estimated by adjusting the illumination using a weight factor, so that bright illumination is compressed to obtain a reflection with approximately uniform illumination. Then logarithmic image processing subtraction (LIPS) is adopted to enhance its contrast. To maintain inter-frame brightness continuity, the background and brightness changes of adjacent frames are measured using optical flow and just noticeable difference (JND) threshold, respectively. If the background and average brightness change little, their reflection brightness is almost the same, so LIPS parameter of previous frame is applied to current frame. Otherwise, current frame will be updated by calculating its own parameter. Experimental results demonstrate that proposed algorithm performs well in brightness continuity and detail enhancement.

The work was supported by the National Nature Science Foundation P.R. China No. 61471201; The first author is a student.

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Authors and Affiliations

  1. Jiangsu Provincial Key Lab of Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Juanjuan Tu, Zongliang Gan & Feng Liu

Authors
  1. Juanjuan Tu
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  2. Zongliang Gan
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  3. Feng Liu
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Corresponding author

Correspondence to Zongliang Gan .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Tu, J., Gan, Z., Liu, F. (2019). Retinex Based Flicker-Free Low-Light Video Enhancement. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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