[1]

Tong P, Yang X, Yang Y, Liu W, Wu P. 2023. Multi-UAV collaborative absolute vision positioning and navigation: a survey and discussion. Drones 7(4):261

doi: 10.3390/drones7040261
[2]

Li Z, Li S, Anderson J, Shan J. 2024. Urban visual localization of block-wise monocular images with google street views. Remote Sensing 16(5):801

doi: 10.3390/rs16050801
[3]

Jiang S, Luo H, Liu Y. 2024. Suitable-matching areas' selection method based on multi-level saliency. Remote Sensing 16(1):161

doi: 10.3390/rs16010161
[4]

He J, Wu Q. 2025. A localization method for UAV aerial images based on semantic topological feature matching. Remote Sensing 17(10):1671

doi: 10.3390/rs17101671
[5]

Liu J, Xu D, Yang W, Fan M, Huang H. 2021. Benchmark lowlight image enhancement and beyond. International Journal of Computer Vision 129(4):1153−1184

doi: 10.1007/s11263-020-01418-8
[6]

Ding B, Sun B, Sun X. 2025. Low-light remote sensing image enhancement via priors guided end-to-end latent residual diffusion. Remote Sensing 17(18):3193

doi: 10.3390/rs17183193
[7]

Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE. 1990. Contrast-limited adaptive histogram equalization: speed and effectiveness. Proceedings of the First Conference on Visualization in Biomedical Computing, May 22−25, 1990, Atlanta, GA, USA. USA: IEEE. pp. 337−338 doi: 10.1109/VBC.1990.109340
[8]

Land EH. 1977. The retinex theory of color vision. Scientific American 237(6):108−129

doi: 10.1038/scientificamerican1277-108
[9]

Cheng HD, Shi XJ. 2004. A simple and effective histogram equalization approach to image enhancement. Digital Signal Processing 14(2):158−170

doi: 10.1016/j.dsp.2003.07.002
[10]

Liu YF, Guo JM, Yu JC. 2017. Contrast enhancement using stratified parametric-oriented histogram equalization. IEEE Transactions on Circuits and Systems for Video Technology 27(6):1171−1181

doi: 10.1109/TCSVT.2016.2527338
[11]

Hao S, Han X, Guo Y, Xu X, Wang M. 2020. Low-light image enhancement with semi-decoupled decomposition. IEEE Transactions on Multimedia 22(12):3025−3038

doi: 10.1109/TMM.2020.2969790
[12]

Ren X, Yang W, Cheng WH, Liu J. 2020. LR3M: robust low-light enhancement via low-rank regularized retinex model. IEEE Transactions on Image Processing 29:5862−5876

doi: 10.1109/tip.2020.2984098
[13]

Wu J, Ai H, Zhou P, Wang H, Zhang H, et al. 2025. Low-light image dehazing and enhancement via multi-feature domain fusion. Remote Sensing 17(17):2944

doi: 10.3390/rs17172944
[14]

Sun Z, Shen Z, Chen N, Pang S, Liu H, et al. 2025. MEFormer: enhancing low-light images while preserving image authenticity in mining environments. Remote Sensing 17(7):1165

doi: 10.3390/rs17071165
[15]

Zhang Y, Guo X, Ma J, Liu W, Zhang J. 2021. Beyond brightening low-light images. International Journal of Computer Vision 129(4):1013−1037

doi: 10.1007/s11263-020-01407-x
[16]

Zhou S, Li C, Change Loy C. 2022. LEDNet: joint low-light enhancement and deblurring in the dark. In Computer Vision – ECCV 2022. Cham, Switzerland: Springer Nature. pp, 573−589 doi: 10.1007/978-3-031-20068-7_33
[17]

Ren W, Liu S, Ma L, Xu Q, Xu X, et al. 2019. Low-light image enhancement via a deep hybrid network. IEEE Transactions on Image Processing 28(9):4364−4375

doi: 10.1109/tip.2019.2910412
[18]

Yao Z, Fan G, Fan J, Gan M, Philip Chen CL. 2024. Spatial–frequency dual-domain feature fusion network for low-light remote sensing image enhancement. IEEE Transactions on Geoscience and Remote Sensing 62:1−16

doi: 10.1109/tgrs.2024.3434416
[19]

Jiang Y, Gong X, Liu D, Cheng Y, Fang C, et al. 2021. EnlightenGAN: deep light enhancement without paired supervision. IEEE Transactions on Image Processing 30:2340−2349

doi: 10.1109/tip.2021.3051462
[20]

Fu Z, Yang Y, Tu X, Huang Y, Ding X, et al. 2023. Learning a simple low-light image enhancer from paired low-light instances. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 17−24, 2023, Vancouver, BC, Canada. USA: IEEE. pp. 22252−22261 doi: 10.1109/cvpr52729.2023.02131
[21]

Ni Z, Yang W, Wang S, Ma L, Kwong S. 2020. Towards unsupervised deep image enhancement with generative adversarial network. IEEE Transactions on Image Processing 29:9140−9151

doi: 10.1109/tip.2020.3023615
[22]

Yang S, Ding M, Wu Y, Li Z, Zhang J. 2023. Implicit neural representation for cooperative low-light image enhancement. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). October 1−6, 2023. Paris, France. USA: IEEE. pp. 12872−12881 doi: 10.1109/iccv51070.2023.01187
[23]

Liu M, Cui Y, Ren W, Zhou J, Knoll AC. 2025. LIEDNet: a lightweight network for low-light enhancement and deblurring. IEEE Transactions on Circuits and Systems for Video Technology 35(7):6602−6615

doi: 10.1109/tcsvt.2025.3541429
[24]

Zhang L, Zhang L, Liu X, Shen Y, Zhang S, et al. 2019. Zero-shot restoration of back-lit images using deep internal learning. Proceedings of the 27th ACM International Conference on Multimedia. October 21−25, 2019, Nice, France. New York, USA: ACM. pp. 1623−1631 doi: 10.1145/3343031.3351069
[25]

Zhu A, Zhang L, Shen Y, Ma Y, Zhao S, et al. 2020. Zero-shot restoration of underexposed images via robust retinex decomposition. 2020 IEEE International Conference on Multimedia and Expo (ICME). July 6−10, 2020, London, UK. USA: IEEE. pp. 1−6 doi: 10.1109/icme46284.2020.9102962
[26]

Guo C, Li C, Guo J, Loy CC, Hou J, et al. 2020. Zero-reference deep curve estimation for low-light image enhancement. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13−19, 2020. Seattle, WA, USA. USA: IEEE. pp. 1777−1786 doi: 10.1109/cvpr42600.2020.00185
[27]

Wang J, Wang R, Wu A. 2019. Improved gamma correction for visual SLAM in low-light scenes. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). 11−13 October 2019, Chongqing, China. USA: IEEE. doi: 10.1109/IMCEC46724.2019.8983904
[28]

Ma L, Ma T, Liu R, Fan X, Luo Z. 2022. Toward fast, flexible, and robust low-light image enhancement. arXiv Preprint

doi: 10.48550/arXiv.2204.10137