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SCDNet: Self-Calibrating Depth Network with Soft-Edge Reconstruction for Low-Light Image Enhancement

Author

Listed:
  • Peixin Qu

    (College of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Zhen Tian

    (College of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Ling Zhou

    (College of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Jielin Li

    (College of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Guohou Li

    (College of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Chenping Zhao

    (School of Mathematical Science, Henan Institute of Science and Technology, Xinxiang 453003, China)

Abstract

Captured low-light images typically suffer from low brightness, low contrast, and blurred details due to the scattering and absorption of light and limited lighting. To deal with these issues, we propose a self-calibrating depth network with soft-edge reconstruction for low-light image enhancement. Concretely, we first employ the soft edge reconstruction module to reconstruct the soft edge of the input image and extract the texture and detail information of the image. Afterward, we explore the convergence properties of each input via the self-calibration module to significantly improve the computational effectiveness of the method and gradually correct the inputs at each subsequent level. Finally, the low-light image is iteratively enhanced by an iterative light enhancement curve to obtain a high-quality image. Extensive experiments demonstrate that our SCDNet visually enhances the brightness and contrast, restores the actual color, and makes the image more in line with the characteristics of the human eye vision system. Meanwhile, our SCDNet outperforms the compared methods in some qualitative and quantitative metrics.

Suggested Citation

  • Peixin Qu & Zhen Tian & Ling Zhou & Jielin Li & Guohou Li & Chenping Zhao, 2023. "SCDNet: Self-Calibrating Depth Network with Soft-Edge Reconstruction for Low-Light Image Enhancement," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1029-:d:1026589
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    References listed on IDEAS

    as
    1. Long Chen & Shuiping Zhang & Haihui Wang & Pengjia Ma & Zhiwei Ma & Gonghao Duan, 2022. "Deep USRNet Reconstruction Method Based on Combined Attention Mechanism," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    2. Zhihao Zhang & Zhitong Su & Wei Song & Keqing Ning, 2022. "Global Attention Super-Resolution Algorithm for Nature Image Edge Enhancement," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    3. Jie Li & Xunxun Zhang & Pei Feng, 2022. "Detection Method of End-of-Life Mobile Phone Components Based on Image Processing," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
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