Journal of Optoelectronics · Laser, Volume. 34, Issue 10, 1059(2023)

Image defogging algorithm based on superpixel dark channel and auto-color optimization

ZHONG Huijuan1,2、*, MA Xiurong2, ZHANG Jingyi1, DONG Yameng1, and LIU Leshan1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    Aiming at problems of halo phenomenon,color distortion of bright areas and inaccurate estimation of ambient light generated by dark channel prior (DCP),a single image defogging algorithm based on superpixel dark channel and auto-color optimization is proposed in this paper.First,the improved White Patch Retinex algorithm is used to enhance the image and calculate the accurate environmental light.Then,the robustness and accuracy of transmission estimation is improved by using superpixel image segmentation and guided filtering algorithm in traditional dark channel defogging algorithm,and adaptive tolerance is used to compensate the transmittance of bright region,which can effectively suppress the color distortion in bright region.Finally,an auto-color optimization algorithm is used to improve image contrast.Comparative experiments of different algorithms are carried out from both subject and object dimensions.Experimental results show that the entropy increases by 0.2 bit,the peak signal-to-noise ratio (PSNR) increases by about 0.8 dB,and the running efficiency is increased using different algorithms for natural fog image with different concentrations.The algorithm has good adaptability to different fog images with different concentrations in different scenes,and the restored images have true colors,clear texture and rich details Defogging effect is good.

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    ZHONG Huijuan, MA Xiurong, ZHANG Jingyi, DONG Yameng, LIU Leshan. Image defogging algorithm based on superpixel dark channel and auto-color optimization[J]. Journal of Optoelectronics · Laser, 2023, 34(10): 1059

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    Paper Information

    Received: May. 8, 2023

    Accepted: --

    Published Online: Sep. 25, 2024

    The Author Email: ZHONG Huijuan (872021753@qq.com)

    DOI:10.16136/j.joel.2023.10.0226

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