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Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

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Abstract

In order to enhance the contrast of low-light images and reduce noise in them, we propose an image enhancement method based on Retinex theory and dual-tree complex wavelet transform (DT-CWT). The method first converts an image from the RGB color space to the HSV color space and decomposes the V-channel by dual-tree complex wavelet transform. Next, an improved local adaptive tone mapping method is applied to process the low frequency components of the image, and a soft threshold denoising algorithm is used to denoise the high frequency components of the image. Then, the V-channel is rebuilt and the contrast is adjusted using white balance method. Finally, the processed image is converted back into the RGB color space as the enhanced result. Experimental results show that the proposed method can effectively improve the performance in terms of contrast enhancement, noise reduction and color reproduction.

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Author information

Authors and Affiliations

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

    Mao-xiang Yang  (杨茂祥), Gui-jin Tang  (唐贵进), Xiao-hua Liu  (刘小花), Li-qian Wang  (王力谦) & Zi-guan Cui  (崔子冠)

  2. School of Electrical Engineering and Computing, University of Newcastle, Newcastle, NSW, 2308, Australia

    Su-huai Luo  (罗苏淮)

Authors
  1. Mao-xiang Yang  (杨茂祥)
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  2. Gui-jin Tang  (唐贵进)
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  3. Xiao-hua Liu  (刘小花)
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  4. Li-qian Wang  (王力谦)
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  5. Zi-guan Cui  (崔子冠)
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  6. Su-huai Luo  (罗苏淮)
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Corresponding author

Correspondence to Gui-jin Tang  (唐贵进).

Additional information

This work has been supported in part by the National Natural Science Foundation of China (Nos.61602257 and 61501260), the Natural Science Foundation of Jiangsu Province (No.BK20160904), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX17_0776), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (No.16KJB520035), and the NUPTSF (Nos.NY214039 and NY215033).

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Yang, Mx., Tang, Gj., Liu, Xh. et al. Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform. Optoelectron. Lett. 14, 470–475 (2018). https://doi.org/10.1007/s11801-018-8046-5

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  • DOI: https://doi.org/10.1007/s11801-018-8046-5

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