• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0237011 (2025)
Yang Tao*, Hao Tan, and Liqun Zhou
Author Affiliations
  • School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • show less
    DOI: 10.3788/LOP241207 Cite this Article Set citation alerts
    Yang Tao, Hao Tan, Liqun Zhou. ULCF-Net: An Underwater Low-Illumination Image Enhancement Algorithm Based on a Cross-Scale Structure and Color Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237011 Copy Citation Text show less
    References

    [1] Li X L. Water related vision[J]. Journal of Electronics, 52, 1041-1082(2024).

    [2] Zhou J C, Yang T Y, Zhang W S. Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends[J]. Applied Intelligence, 53, 3594-3621(2023).

    [3] Guo W, Zhang Y B, Zhou Y et al. Rapid deep-sea image restoration algorithm applied to unmanned underwater vehicles[J]. Acta Optica Sinica, 42, 0410002(2022).

    [4] Guo Y J, Wu Q, Yuan J J et al. Research progress on underwater optical image processing[J]. Journal of Electronics & Information Technology, 43, 426-435(2021).

    [5] Fu X Y, Fan Z W, Ling M et al. Two-step approach for single underwater image enhancement[C], 789-794(2017).

    [6] Li C Y, Guo J C, Cong R M et al. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J]. IEEE Transactions on Image Processing, 25, 5664-5677(2016).

    [7] Liang Z, Ding X Y, Wang Y F et al. GUDCP: generalization of underwater dark channel prior for underwater image restoration[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 4879-4884(2022).

    [8] Drews P, Jr, do Nascimento E, Moraes F et al. Transmission estimation in underwater single images[C], 825-830(2013).

    [9] Wang P Z, Zhang S K, Zhang K et al. Underwater image enhancement of nuclear power plant based on U-net model[C](2023).

    [10] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention- MICCAI 2015, 9351, 234-241(2015).

    [11] Liu G D, Feng L H, Lu J H et al. Underwater image restoration based on classification and dark channel prior with minimum convolutional area[J]. Laser & Optoelectronics Progress, 60, 0401003(2023).

    [12] Guo Y C, Li H Y, Zhuang P X. Underwater image enhancement using a multiscale dense generative adversarial network[J]. IEEE Journal of Oceanic Engineering, 45, 862-870(2020).

    [13] Zhao C, Cai W L, Dong C Y et al. Toward sufficient spatial-frequency interaction for gradient-aware underwater image enhancement[EB/OL]. http://arxiv.org/abs/2309.04089v2

    [14] Jiang J X, Ye T, Bai J B et al. Five A+ network: you only need 9K parameters for underwater image enhancement[EB/OL]. http://arxiv.org/abs/2305.08824v1

    [15] Ma M H, Wang H R, Wang J. An underwater image enhancement algorithm based on improved MSRCR-CLAHE fusion[J]. Infrared Technology, 45, 23-32(2023).

    [16] Qu J X, Liu R W, Gao Y et al. Double domain guided real-time low-light image enhancement for ultra-high-definition transportation surveillance[J]. IEEE Transactions on Intelligent Transportation Systems, 25, 9550-9562(2024).

    [17] Hai J, Xuan Z, Yang R et al. R2RNet: Low-light image enhancement via real-low to real-normal network[J]. Journal of Visual Communication and Image Representation, 90, 103712(2023).

    [18] Fan C M, Liu T J, Liu K H. Half wavelet attention on M-Net+ for low-light image enhancement[C], 3878-3882(2022).

    [19] Wang C X, Wu H J, Jin Z. FourLLIE: boosting low-light image enhancement by Fourier frequency information[C], 7459-7469(2023).

    [20] Han Y C, Zhang W W, He W J et al. Low-light true color image enhancement algorithm based on adaptive truncation simulation exposure and unsupervised fusion[J]. Acta Photonica Sinica, 52, 0910002(2023).

    [21] Xie Y F, Yu Z B, Yu X et al. Lighting the darkness in the sea: a deep learning model for underwater image enhancement[J]. Frontiers in Marine Science, 9, 921492(2022).

    [22] Huang J, Zhou M, Li D et al. Revitalizing channel-dimension fourier transform for image enhancement[EB/OL]. https://openreview.net/forum?id=3tjTJeXyA7

    [23] Zamir S W, Arora A, Khan S et al. Learning enriched features for real image restoration and enhancement[M]. Computer vision-ECCV 2020, 12370, 492-511(2020).

    [24] Lim C C, Loh Y P, Wong L K. LAU-Net: a low light image enhancer with attention and resizing mechanisms[J]. Signal Processing: Image Communication, 115, 116971(2023).

    [25] Cai Y H, Bian H, Lin J et al. Retinexformer: one-stage Retinex-based transformer for low-light image enhancement[C], 12470-12479(2023).

    [26] Zhao Q H, Zhang X F, Tang H et al. Enlighten Anything: when segment anything model meets low-light image enhancement[EB/OL]. http://arxiv.org/abs/2306.10286v4

    [27] Shakibania H, Raoufi S, Khotanlou H. CDAN: convolutional dense attention-guided network for low-light image enhancement[EB/OL]. http://arxiv.org/abs/2308.12902v2

    [28] Peng L T, Zhu C L, Bian L H. U-shape transformer for underwater image enhancement[J]. IEEE Transactions on Image Processing, 32, 3066-3079(2023).

    [29] Liu S B, Fan H J, Lin S et al. Adaptive learning attention network for underwater image enhancement[J]. IEEE Robotics and Automation Letters, 7, 5326-5333(2022).

    [30] Wang D, Ma L, Liu R S et al. Semantic-aware texture-structure feature collaboration for underwater image enhancement[C], 4592-4598(2022).

    [31] Naik A, Swarnakar A, Mittal K. Shallow-UWnet: compressed model for underwater image enhancement (student abstract)[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 15853-15854(2021).

    Yang Tao, Hao Tan, Liqun Zhou. ULCF-Net: An Underwater Low-Illumination Image Enhancement Algorithm Based on a Cross-Scale Structure and Color Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237011
    Download Citation