| Citation: |
LYU Zongwang, NIU Hejie, SUN Fuyan, ZHEN Tong. Review of Research on Low-Light Image Enhancement Algorithms[J]. Infrared Technology , 2025, 47(2): 165-178.
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Low-light image enhancement is an important problem in the field of image processing. The rapid development of deep learning technology provides a new solution for low-light image enhancement and has broad application prospects. First, the current research status and challenges in the field of low-light image enhancement are comprehensively analyzed, and traditional methods and their advantages and disadvantages are introduced. Second, deep learning-based low-light image enhancement algorithms are classified into five categories according to their different learning strategies, and the principles, network structures, and problem-solving capabilities of these algorithms are explained in detail. Third, representative deep learning-based image enhancement algorithms from the last six years are compared and analyzed in chronological order. Fourth, the current mainstream datasets and evaluation indexes are summarized, and the deep learning algorithms are tested and evaluated in terms of perceived similarity and algorithm performance. Finally, directions for improvement and future research in the field of low-light image enhancement are discussed and suggested.
| [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. DOI: 10.1007/s11263-020-01418-8
|
| [2] |
郭永坤, 朱彦陈, 刘莉萍, 等. 空频域图像增强方法研究综述[J]. 计算机工程与应用, 2022, 58(11): 23-32.
GUO Y K, ZHU Y C, LIU L P, et al. A review of research on image enhancement methods in the air-frequency domain[J]. Computer Engineering and Application, 2022, 58(11): 23-32.
|
| [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. DOI: 10.1007/s00500-021-06539-x
|
| [4] |
杨嘉能, 李华, 田宸玮, 等. 基于自适应校正的动态直方图均衡算法[J]. 计算机工程与设计, 2021, 42(5): 1264-1270.
YANG J N, LI H, TIAN C W, et al. Dynamic histogram equalization algorithm based on adaptive correction[J]. Computer Engineering and Design, 2021, 42(5): 1264-1270.
|
| [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. DOI: 10.1002/ima.22307
|
| [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. DOI: 10.1007/s11042-022-12407-z
|
| [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. DOI: 10.1109/ACCESS.2021.3112046
|
| [8] |
陈迎春. 基于色调映射的快速低照度图像增强[J]. 计算机工程与应用, 2020, 56(9): 234-239.
CHEN Y C. Fast low-light image enhancement based on tone mapping[J]. Computer Engineering and Applications, 2020, 56(9): 234-239.
|
| [9] |
赵海法, 朱荣, 杜长青, 等. 全局色调映射和局部对比度处理相结合的图像增强算法[J]. 武汉大学学报, 2020, 66(6): 597-604.
ZHAO H F, ZHU R, DU C Q, et al. An image enhancement algorithm combining global tone mapping and local contrast processing[J]. Journal of Wuhan University, 2020, 66(6): 597-604.
|
| [10] |
李明悦, 晏涛, 井花花, 等. 多尺度特征融合的低照度光场图像增强算法[J]. 计算机科学与探索, 2022, 17(8): 1904-1916.
LI M Y, YAN T, JING H H, et al. Multi-scale feature fusion algorithm for low illumination light field image enhancement[J]. Computer Science and Exploration, 2022, 17(8): 1904-1916.
|
| [11] |
张微微. 基于图像融合的低照度水下图像增强[D]. 大连: 大连海洋大学, 2023.
ZHANG W W. Low Illumination Underwater Image Enhancement Based on Image Fusion[D]. Dalian: Dalian Ocean University, 2023.
|
| [12] |
田子建, 吴佳奇, 张文琪, 等. 基于Transformer和自适应特征融合的矿井低照度图像亮度提升和细节增强方法[J]. 煤炭科学技术, 2024, 52(1): 297-310.
TIAN Z J, WU J Q, ZHANG W Q, et al. Brightness enhancement and detail enhancement method for low illumination images of mines based on Transformer and adaptive feature fusion[J]. Coal Science and Technology, 2024, 52(1): 297-310.
|
| [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] |
HUO Y S. Polarization-based research on a priori defogging of dark channel[J]. Acta Physica Sinica, 2022, 71(14): 144202. DOI: 10.7498/aps.71.20220332
|
| [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. DOI: 10.1109/ACCESS.2021.3086191
|
| [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. DOI: 10.1007/s11042-022-13237-9
|
| [17] |
LAND E H. The retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108-129. DOI: 10.1038/scientificamerican1277-108
|
| [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] |
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] |
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. DOI: 10.1109/TIP.2020.2984098
|
| [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. DOI: 10.1016/j.patcog.2016.06.008
|
| [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] |
ZHANG Y, GUO X, MA J, et al. Beyond brightening low-light images[J]. International Journal of Computer Vision, 2021: 1013-1037.
|
| [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. DOI: 10.1016/j.patrec.2018.01.010
|
| [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. DOI: 10.1109/TMM.2020.3037526
|
| [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. DOI: 10.1109/TIP.2021.3051462
|
| [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. DOI: 10.1016/j.displa.2023.102550
|
| [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] |
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.
|
| [30] |
周腾威. 基于深度学习的图像增强算法研究[D]. 南京: 南京信息工程大学, 2021.
ZHOU T W. Research on Image Enhancement Algorithm Based on Deep Learning[D]. Nanjing: Nanjing University of Information Engineering, 2021.
|
| [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] |
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] |
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] |
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/ICME46284.2020.9102962.
|
| [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] |
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] |
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] |
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] |
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] |
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] |
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] |
Cheikh Sidiyadiya A. Generative prior for unsupervised image restoration[D]. Ahmed Cheikh Sidiya: West Virginia University, 2023.
|
| [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.
|
| [44] |
张箴, 鹿阳, 苏奕铭, 等. 基于多通道Retinex模型的低照度图像增强网络[J]. 信息与控制, 2024, 53(5): 652-661.
ZHANG Z, LU Y, SU Y M, et al. Low-light image enhancement network based on multi-channel Retinex model[J]. Information and Control, 2024, 53(5): 652-661.
|
| [45] |
BAI J, YIN Y, HE Q. Retinexmamba: retinex-based mamba for low-light image enhancement[J]. arXiv preprint arXiv: 2405.03349, 2024.
|
| [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] |
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] |
WEI C, WANG W, YANG W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv: 1808.04560, 2018.
|
| [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] |
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] |
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] |
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] |
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] |
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] |
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] |
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] |
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] |
HU S, YAN J, DENG D. Contextual information aided generative adversarial network for low-light image enhancement[J]. Electronics, 2021, 11(1): 32.
|
| [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] |
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] |
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] |
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] |
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] |
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] |
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] |
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.
|
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