Infrared Technology, Volume. 47, Issue 2, 165(2025)

Review of Research on Low-Light Image Enhancement Algorithms

Zongwang LYU1,2、*, Hejie NIU1,2, Fuyan SUN1,2, and Tong ZHEN1,2
Author Affiliations
  • 1School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
  • 2Key Laboratory of Grain Information Processing and Control, Ministry of Education, Zhengzhou 450001, China
  • show less
    References(57)

    [1] [1] LIU J, XU D, YANG W, et al. Benchmarking low-light image enhancement and beyond[J]. International Journal of Computer Vision, 2021, 129: 1153-1184.

    [3] [3] Jebadass J R, Balasubramaniam P. Low contrast enhancement technique for color images using intervalvalued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization[J]. Soft Computing, 2022, 26(10): 4949-4960.

    [5] [5] KUO C F J, WU H C. Gaussian probability bi-histogram equalization for enhancement of the pathological features in medical images[J]. International Journal of Imaging Systems and Technology, 2019, 29(2): 132-145.

    [6] [6] LI C, LIU J, ZHU J, et al. Mine image enhancement using adaptive bilateral gamma adjustment and double plateaus histogram equalization[J]. Multimedia Tools and Applications, 2022, 81(9): 12643-12660.

    [7] [7] Nguyen N H, Vo T V, Lee C. Human visual system model-based optimized tone mapping of high dynamic range images[J]. IEEE Access, 2021, 9: 127343-127355.

    [13] [13] DONG X, PANG Y, WEN J, et al. Fast efficient algorithm for enhancement of low lighting video[C]//2011 IEEE International Conference on Multimedia and Expo, 2011: 1-6.

    [14] [14] HUO Y S. Polarization-based research on a priori defogging of dark channel[J]. Acta Physica Sinica, 2022, 71(14): 144202.

    [15] [15] HONG S, KIM M, LEE H, et al. Nighttime single image dehazing based on the structural patch decomposition[J]. IEEE Access, 2021, 9: 82070-82082.

    [16] [16] SI Y, YANG F, CHONG N. A novel method for single nighttime image haze removal based on gray space[J]. Multimedia Tools and Applications, 2022, 81(30): 43467-43484.

    [17] [17] LAND E H. The retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108-129.

    [18] [18] WANG R, ZHANG Q, FU C W, et al. Underexposed photo enhancement using deep illumination estimation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 6849-6857.

    [19] [19] CAI Y, BIAN H, LIN J, et al. Retinexformer: one-stage retinex-based transformer for low-light image enhancement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 12504-12513.

    [20] [20] REN X, YANG W, CHENG W H, et al. LR3M: robust low-light enhancement via low-rank regularized retinex model[J]. IEEE Transactions on Image Processing, 2020, 29: 5862-5876.

    [21] [21] Lore K G, Akintayo A, Sarkar S. LLNet: a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 2017, 61: 650-662.

    [22] [22] ZHANG Y, ZHANG J, GUO X. Kindling the darkness: a practical low-light image enhancer[C]//Proceedings of the 27th ACM International Conference on Multimedia, 2019: 1632-1640.

    [23] [23] ZHANG Y, GUO X, MA J, et al. Beyond brightening low-light images[J]. International Journal of Computer Vision, 2021: 1013-1037.

    [24] [24] LI C, GUO J, PORIKLI F, et al. LightenNet: a convolutional neural network for weakly illuminated image enhancement[J]. Pattern Recognition Letters, 2018, 104: 15-22.

    [25] [25] LU K, ZHANG L. TBEFN: a two-branch exposure-fusion network for low-light image enhancement[J]. IEEE Transactions on Multimedia, 2021, 23: 4093-4105.

    [26] [26] JIANG Y, GONG X, LIU D, et al. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 2021, 30: 2340-2349.

    [27] [27] LI X, HE R, WU J, et al. LEES-Net: fast, lightweight unsupervised curve estimation network for low-light image enhancement and exposure suppression[J]. Displays, 2023, 80: 102550.

    [28] [28] YANG S, DING M, WU Y, et al. Implicit neural representation for cooperative low-light image enhancement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 12918-12927.

    [29] [29] YU R, LIU W, ZHANG Y, et al. Deepexposure: learning to expose photos with asynchronously reinforced adversarial learning[J]. Advances in Neural Information Processing Systems, 2018, 31: 7429-7439.

    [31] [31] ZHANG L, ZHANG L, LIU X, et al. Zero-shot restoration of back-lit images using deep internal learning[C]//Proceedings of the 27th ACM International Conference on Multimedia, 2019: 1623-1631.

    [32] [32] GUO C, LI C, GUO J, et al. Zero-reference deep curve estimation for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1780-1789.

    [33] [33] LI C, GUO C, CHEN C L. Learning to enhance low-light image via zero-reference deep curve estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(8): 4225- 4238.

    [34] [34] ZHU A, ZHANG L, SHEN Y, et al. Zero-shot restoration of underexposed images via robust retinex decomposition[C]//2020 IEEE International Conference on Multimedia and Expo (ICME), 2020, DOI: 10.1109/ICME46284.2020.910296210.1109/ICME46284.2020.9102962.

    [35] [35] SOHN K, BERTHELOT D, CARLINI N, et al. Fixmatch: simplifying semi-supervised learning with consistency and confidence[J]. Advances in Neural Information Processing Systems, 2020, 33: 596-608.

    [36] [36] LIU Y, TIAN Y, CHEN Y, et al. Perturbed and strict mean teachers for semi-supervised semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 4258-4267.

    [37] [37] YANG W, WANG S, FANG Y, et al. From fidelity to perceptual quality: a semi-supervised approach for low-light image enhancement [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 3063-3072.

    [38] [38] LIU R, MA L, ZHANG J, et al. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10561-10570.

    [39] [39] ZHOU S, LI C, CHANGE LOY C. LEDNet: joint low-light enhancement and deblurring in the dark[C]//European Conference on Computer Vision, 2022: 573-589.

    [40] [40] ZHAO Y, XU Y, YAN Q, et al. D2hnet: Joint denoising and deblurring with hierarchical network for robust night image restoration [C]//European Conference on Computer Vision, 2022: 91-110.

    [41] [41] XU X, WANG R, LU J. Low-Light Image Enhancement via Structure Modeling and Guidance[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 9893-9903.

    [42] [42] Cheikh Sidiyadiya A. Generative prior for unsupervised image restoration[D]. Ahmed Cheikh Sidiya: West Virginia University, 2023.

    [43] [43] LIU X, XIE Q, ZHAO Q, et al. Low-light image enhancement by retinex-based algorithm unrolling and adjustment[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(11): 2162-2388.

    [45] [45] BAI J, YIN Y, HE Q. Retinexmamba: retinex-based mamba for low-light image enhancement[J]. arXiv preprint arXiv: 2405.03349, 2024.

    [46] [46] Brateanu A, Balmez R, Avram A, et al. Lyt-net: lightweight yuv transformer-based network for low-light image enhancement[J]. arXiv preprint arXiv: 2401.15204, 2024.

    [47] [47] CAI J, GU S, ZHANG L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.

    [48] [48] WEI C, WANG W, YANG W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv: 1808.04560, 2018.

    [49] [49] LIU J, XU D, YANG W, et al. Benchmarking low-light image enhancement and beyond[J]. International Journal of Computer Vision, 2021, 129: 1153-1184.

    [50] [50] Bychkovsky V, Paris S, CHAN E, et al. Learning photographic global tonal adjustment with a database of input/output image pairs[C]//CVPR of IEEE, 2011: 97-104.

    [51] [51] CHEN C, CHEN Q, XU J, et al. Learning to see in the dark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3291-3300.

    [52] [52] JIANG H, ZHENG Y. Learning to see moving objects in the dark[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 7324-7333.

    [53] [53] GUO X, LI Y, LING H. LIME: Low-light image enhancement via Illumination Map Estimation[J]. IEEE Transactions on Image Processing, 2017, 26(2): 982-993.

    [54] [54] LOH Y P, CHAN C S. Getting to know low-light images with the exclusively dark dataset[J]. Computer Vision and Image Understanding, 2019, 178: 30-42.

    [55] [55] SARA U, AKTER M, UDDIN M S. Image quality assessment through FSIM, SSIM, MSE and PSNR——a comparative study[J]. Journal of Computer and Communications, 2019, 7(3): 8-18.

    [56] [56] Mittal A, Soundararajan R, Bovik A C. Making a “Completely Blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209-212.

    [57] [57] ZHANG R, Isola P, Efros A A, et al. The unreasonable effectiveness of deep features as a perceptual metric[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 586-595.

    [58] [58] HU S, YAN J, DENG D. Contextual information aided generative adversarial network for low-light image enhancement[J]. Electronics, 2021, 11(1): 32.

    [59] [59] YANG S, ZHOU D, CAO J, et al. Rethinking low-light enhancement via transformer-GAN[J]. IEEE Signal Processing Letters, 2022, 29: 1082-1086.

    [60] [60] PAN Z, YUAN F, LEI J, et al. MIEGAN: mobile image enhancement via a multi-module cascade neural network[J]. IEEE Transactions on Multimedia, 2022, 24: 519-533.

    [61] [61] CHEN X, LI J, HUA Z. Retinex low-light image enhancement network based on attention mechanism[J]. Multimedia Tools and Applications, 2023, 82(3): 4235-4255.

    [62] [62] ZHANG Q, ZOU C, SHAO M, et al. A single-stage unsupervised denoising low-illumination enhancement network based on swin-transformer[J]. IEEE Access, 2023, 11: 75696-75706.

    [63] [63] YE J, FU C, CAO Z, et al. Tracker meets night: a transformer enhancer for UAV tracking[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 3866-3873.

    [64] [64] Kanev A, Nazarov M, Uskov D, et al. Research of different neural network architectures for audio and video denoising[C]//2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) of IEEE, 2023, 5: 1-5.

    [65] [65] FENG X, LI J, HUA Z. Low-light image enhancement algorithm based on an atmospheric physical model[J]. Multimedia Tools and Applications, 2020, 79(43): 32973-32997.

    [66] [66] JIA D, YANG J. A multi-scale image enhancement algorithm based on deep learning and illumination compensation[J]. Traitement du Signal, 2022, 39(1): 179-185.

    Tools

    Get Citation

    Copy Citation Text

    LYU Zongwang, NIU Hejie, SUN Fuyan, ZHEN Tong. Review of Research on Low-Light Image Enhancement Algorithms[J]. Infrared Technology, 2025, 47(2): 165

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Aug. 7, 2024

    Accepted: Mar. 13, 2025

    Published Online: Mar. 13, 2025

    The Author Email: LYU Zongwang (zongwang_lv@126.com)

    DOI:

    Topics