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It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods.<\/jats:p>","DOI":"10.3390\/rs14184608","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T01:35:10Z","timestamp":1663292110000},"page":"4608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["An Empirical Study on Retinex Methods for Low-Light Image Enhancement"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-4688","authenticated-orcid":false,"given":"Muhammad Tahir","family":"Rasheed","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiyu","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daming","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0037-1448","authenticated-orcid":false,"given":"Hufsa","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0371-9646","authenticated-orcid":false,"given":"Xiaochun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Computer Science Department, Middlesex University, Hendon, London NW4 4BT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TMM.2016.2544099","article-title":"CSPS: An adaptive pooling method for image classification","volume":"18","author":"Wang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., and Ling, H. (2019, January 8\u201312). M2det: A single-shot object detector based on multi-level feature pyramid network. Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, USA.","DOI":"10.1609\/aaai.v33i01.33019259"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016, January 8\u201314). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, A., Luo, C., Tian, X., and Zeng, W. (2018, January 18\u201322). A twofold siamese network for real-time object tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00508"},{"key":"ref_6","unstructured":"Luo, W., Sun, P., Zhong, F., Liu, W., Zhang, T., and Wang, Y. (2018, January 10\u201315). End-to-end active object tracking via reinforcement learning. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ristani, E., and Tomasi, C. (2018, January 18\u201323). Features for multi-target multi-camera tracking and re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00632"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/TMM.2012.2186957","article-title":"Adaptive workload equalization in multi-camera surveillance systems","volume":"14","author":"Saini","year":"2012","journal-title":"IEEE Trans. Multimed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Feng, W., Ji, D., Wang, Y., Chang, S., Ren, H., and Gan, W. (2018, January 18\u201323). Challenges on large scale surveillance video analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00017"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6392","DOI":"10.1109\/TIE.2017.2682034","article-title":"Artifact-free low-light video enhancement using temporal similarity and guide map","volume":"64","author":"Ko","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.neucom.2022.07.058","article-title":"LSR: Lightening super-resolution deep network for low-light image enhancement","volume":"505","author":"Rasheed","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ins.2022.02.051","article-title":"Handling missing data through deep convolutional neural network","volume":"595","author":"Khan","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.compeleceng.2021.107230","article-title":"Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering","volume":"93","author":"Khan","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43674-021-00015-7","article-title":"Missing label imputation through inception-based semi-supervised ensemble learning","volume":"2","author":"Khan","year":"2022","journal-title":"Adv. Comput. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1175\/1520-0434(1995)010<0606:AITDAA>2.0.CO;2","article-title":"Advances in the detection and analysis of fog at night using GOES multispectral infrared imagery","volume":"10","author":"Ellrod","year":"1995","journal-title":"Weather. Forecast."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TITS.2015.2405013","article-title":"Exponential contrast restoration in fog conditions for driving assistance","volume":"16","author":"Negru","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","article-title":"The retinex theory of color vision","volume":"237","author":"Land","year":"1977","journal-title":"Sci. Am."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/83.557356","article-title":"Properties and performance of a center\/surround retinex","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/83.597272","article-title":"A multiscale retinex for bridging the gap between color images and the human observation of scenes","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","unstructured":"Fu, X., Zeng, D., Huang, Y., Zhang, X.P., and Ding, X. (July, January 26). A weighted variational model for simultaneous reflectance and illumination estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-light image enhancement via illumination map estimation","volume":"26","author":"Guo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4093","DOI":"10.1109\/TMM.2020.3037526","article-title":"TBEFN: A two-branch exposure-fusion network for low-light image enhancement","volume":"23","author":"Lu","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_24","unstructured":"Zhang, Y., Di, X., Zhang, B., Li, Q., Yan, S., and Wang, C. (2021). Self-supervised Low Light Image Enhancement and Denoising. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., and Zhou, Y. (2020, January 6\u201310). Zero-shot restoration of underexposed images via robust retinex decomposition. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102962"},{"key":"ref_26","unstructured":"Gonzalez, R.C. (1992). Digital Image Processing, Addison-Wesley. [2nd ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TCE.2007.381734","article-title":"A dynamic histogram equalization for image contrast enhancement","volume":"53","author":"Kabir","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_28","first-page":"2016","article-title":"An adaptive gamma correction for image enhancement","volume":"35","author":"Rahman","year":"2016","journal-title":"EURASIP J. Image Video Process"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TIP.2012.2226047","article-title":"Efficient contrast enhancement using adaptive gamma correction with weighting distribution","volume":"22","author":"Huang","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.displa.2009.03.006","article-title":"A real-time image processor with combining dynamic contrast ratio enhancement and inverse gamma correction for PDP","volume":"30","author":"Wang","year":"2009","journal-title":"Displays"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","article-title":"LLNet: A deep autoencoder approach to natural low-light image enhancement","volume":"61","author":"Lore","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., and Koltun, V. (2018, January 18\u201323). Learning to see in the dark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00347"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TIP.2018.2794218","article-title":"Learning a deep single image contrast enhancer from multi-exposure images","volume":"27","author":"Cai","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021, January 10\u201317). LLVIP: A Visible-infrared Paired Dataset for Low-light Vision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, J.Y., Yoo, D., and Kweon, I.S. (2018, January 18\u201323). Distort-and-recover: Color enhancement using deep reinforcement learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00621"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., and Guo, X. (2019, January 21\u201325). Kindling the darkness: A practical low-light image enhancer. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350926"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zheng, C., Shi, D., and Shi, W. (2021, January 10\u201317). Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00440"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.P., and Kot, A.C. (2021). Low-Light Image Enhancement with Normalizing Flow. arXiv.","DOI":"10.1609\/aaai.v36i3.20162"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"87884","DOI":"10.1109\/ACCESS.2020.2992749","article-title":"An experiment-based review of low-light image enhancement methods","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s11831-021-09587-6","article-title":"A Comprehensive Overview of Image Enhancement Techniques","volume":"29","author":"Qi","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, C., Han, L.H., Jiang, J., Cheng, M.M., Gu, J., and Loy, C.C. (2021). Low-light image and video enhancement using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 1.","DOI":"10.1109\/TPAMI.2021.3126387"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1364\/JOSA.61.000001","article-title":"Lightness and retinex theory","volume":"61","author":"Land","year":"1971","journal-title":"Josa"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.1073\/pnas.80.16.5163","article-title":"Recent advances in retinex theory and some implications for cortical computations: Color vision and the natural image","volume":"80","author":"Land","year":"1983","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1364\/JOSAA.22.002613","article-title":"Mathematical definition and analysis of the Retinex algorithm","volume":"22","author":"Provenzi","year":"2005","journal-title":"JOSA A"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/S0262-8856(00)00037-8","article-title":"A computational approach to color adaptation effects","volume":"18","author":"Marini","year":"2000","journal-title":"Image Vis. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1073\/pnas.83.10.3078","article-title":"An alternative technique for the computation of the designator in the retinex theory of color vision","volume":"83","author":"Land","year":"1986","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1117\/1.1636182","article-title":"Analysis and extensions of the Frankle-McCann Retinex algorithm","volume":"13","author":"Cooper","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TIP.2006.884946","article-title":"Random spray Retinex: A new Retinex implementation to investigate the local properties of the model","volume":"16","author":"Provenzi","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4965","DOI":"10.1109\/TIP.2015.2474701","article-title":"A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation","volume":"24","author":"Fu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","article-title":"Naturalness preserved enhancement algorithm for non-uniform illumination images","volume":"22","author":"Wang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1137\/140972664","article-title":"Non-Local Retinex\u2014A Unifying Framework and Beyond","volume":"8","author":"Zosso","year":"2015","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1022314423998","article-title":"A variational framework for retinex","volume":"52","author":"Kimmel","year":"2003","journal-title":"Int. J. Comput. Vis."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"697","DOI":"10.3934\/ipi.2012.6.697","article-title":"A TV Bregman iterative model of Retinex theory","volume":"6","author":"Ma","year":"2012","journal-title":"Inverse Probl. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ma, W., Morel, J.M., Osher, S., and Chien, A. (2011, January 20\u201325). An L 1-based variational model for Retinex theory and its application to medical images. Proceedings of the CVPR, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995422"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fu, X., Zeng, D., Huang, Y., Ding, X., and Zhang, X.P. (2013, January 3\u20135). A variational framework for single low light image enhancement using bright channel prior. Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA.","DOI":"10.1109\/GlobalSIP.2013.6737082"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1137\/100806588","article-title":"A total variation model for Retinex","volume":"4","author":"Ng","year":"2011","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.sigpro.2016.05.031","article-title":"A fusion-based enhancing method for weakly illuminated images","volume":"129","author":"Fu","year":"2016","journal-title":"Signal Process."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Cai, B., Xu, X., Guo, K., Jia, K., Hu, B., and Tao, D. (2017, January 22\u201329). A joint intrinsic-extrinsic prior model for retinex. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.431"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ying, Z., Li, G., Ren, Y., Wang, R., and Wang, W. (2017, January 22\u201329). A new low-light image enhancement algorithm using camera response model. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.356"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s11263-020-01407-x","article-title":"Beyond brightening low-light images","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_61","unstructured":"Hai, J., Xuan, Z., Yang, R., Hao, Y., Zou, F., Lin, F., and Han, S. (2021). R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, R., Ma, L., Zhang, J., Fan, X., and Luo, Z. (2021, January 20\u201325). Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01042"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, R., Xu, X., Fu, C.W., Lu, J., Yu, B., and Jia, J. (2021, January 10\u201317). Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00956"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TCSVT.2021.3073371","article-title":"RetinexDIP: A Unified Deep Framework for Low-light Image Enhancement","volume":"32","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_65","unstructured":"Yu, R., Liu, W., Zhang, Y., Qu, Z., Zhao, D., and Zhang, B. (2018, January 3\u20138). Deepexposure: Learning to expose photos with asynchronously reinforced adversarial learning. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.dsp.2003.07.002","article-title":"A simple and effective histogram equalization approach to image enhancement","volume":"14","author":"Cheng","year":"2004","journal-title":"Digit. Signal Process."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/30.580378","article-title":"Contrast enhancement using brightness preserving bi-histogram equalization","volume":"43","author":"Kim","year":"1997","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TCE.2007.4429280","article-title":"Brightness preserving dynamic histogram equalization for image contrast enhancement","volume":"53","author":"Ibrahim","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Guan, X., Jian, S., Hongda, P., Zhiguo, Z., and Haibin, G. (2009, January 12\u201314). An image enhancement method based on gamma correction. Proceedings of the 2009 Second International Symposium on Computational Intelligence and Design, Changsha, China.","DOI":"10.1109\/ISCID.2009.22"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Tao, L., Zhu, C., Xiang, G., Li, Y., Jia, H., and Xie, X. (2017, January 10\u201313). LLCNN: A convolutional neural network for low-light image enhancement. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305143"},{"key":"ref_71","unstructured":"Lv, F., Lu, F., Wu, J., and Lim, C. (2018, January 3\u20136). MBLLEN: Low-Light Image\/Video Enhancement Using CNNs. Proceedings of the BMVC, Newcastle, UK."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, W., Wei, C., Yang, W., and Liu, J. (2018, January 15\u201318). GLADNet: Low-light enhancement network with global awareness. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Jodhpur, India.","DOI":"10.1109\/FG.2018.00118"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"Enlightengan: Deep light enhancement without paired supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_74","unstructured":"Xiong, W., Liu, D., Shen, X., Fang, C., and Luo, J. (2020). Unsupervised real-world low-light image enhancement with decoupled networks. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Xia, Z., Gharbi, M., Perazzi, F., Sunkavalli, K., and Chakrabarti, A. (2021, January 20\u201325). Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00210"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Le, H.A., and Kakadiaris, I.A. (2019, January 4\u20137). SeLENet: A semi-supervised low light face enhancement method for mobile face unlock. Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece.","DOI":"10.1109\/ICB45273.2019.8987344"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 13\u201319). From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00313"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Qiao, Z., Xu, W., Sun, L., Qiu, S., and Guo, H. (2021, January 23\u201325). Deep Semi-Supervised Learning for Low-Light Image Enhancement. Proceedings of the 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Online.","DOI":"10.1109\/CISP-BMEI53629.2021.9624226"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wu, W., Wang, W., Jiang, K., Xu, X., and Hu, R. (2022, January 22\u201327). Self-Supervised Learning on A Lightweight Low-Light Image Enhancement Model with Curve Refinement. Proceedings of the ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9746348"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Guo, C.G., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","article-title":"Lightening network for low-light image enhancement","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5372","DOI":"10.1109\/TIP.2013.2284059","article-title":"Contrast enhancement based on layered difference representation of 2D histograms","volume":"22","author":"Lee","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.1109\/TIP.2015.2442920","article-title":"Perceptual quality assessment for multi-exposure image fusion","volume":"24","author":"Ma","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_84","unstructured":"Lv, F., Li, Y., and Lu, F. (2019). Attention guided low-light image enhancement with a large scale low-light simulation dataset. arXiv."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.cviu.2018.10.010","article-title":"Getting to know low-light images with the exclusively dark dataset","volume":"178","author":"Loh","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Gonzalez, R.C. (2009). Digital Image Processing, Pearson Education India.","DOI":"10.1117\/1.3115362"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_89","unstructured":"Papasaika-Hanusch, H. (1967). Digital image PROCESSING Using Matlab, Institute of Geodesy and Photogrammetry, ETH Zurich."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1109\/TIP.2011.2157513","article-title":"Contextual and variational contrast enhancement","volume":"20","author":"Celik","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_91","unstructured":"Pizer, S.M. (1990, January 22\u201325). Contrast-limited adaptive histogram equalization: Speed and effectiveness stephen m. pizer, r. eugene johnston, james p. ericksen, bonnie c. yankaskas, keith e. muller medical image display research group. Proceedings of the First Conference on Visualization in Biomedical Computing, Atlanta, GA, USA."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.compeleceng.2017.09.012","article-title":"Contrast enhancement of brightness-distorted images by improved adaptive gamma correction","volume":"66","author":"Cao","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_93","unstructured":"Ying, Z., Li, G., and Gao, W. (2017). A bio-inspired multi-exposure fusion framework for low-light image enhancement. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Afifi, M., Derpanis, K.G., Ommer, B., and Brown, M.S. (2021, January 20\u201325). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00904"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhang, F., Li, Y., You, S., and Fu, Y. (2021, January 20\u201325). Learning Temporal Consistency for Low Light Video Enhancement From Single Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00493"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","article-title":"LightenNet: A convolutional neural network for weakly illuminated image enhancement","volume":"104","author":"Li","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_97","first-page":"1","article-title":"Exposure: A white-box photo post-processing framework","volume":"37","author":"Hu","year":"2018","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ying, Z., Li, G., Ren, Y., Wang, R., and Wang, W. (2017, January 22\u201324). A new image contrast enhancement algorithm using exposure fusion framework. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64698-5_4"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1049\/iet-cvi.2017.0259","article-title":"Perceptually motivated enhancement method for non-uniformly illuminated images","volume":"12","author":"Pu","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1049\/iet-ipr.2018.6585","article-title":"Nighttime image enhancement using a new illumination boost algorithm","volume":"13","year":"2019","journal-title":"IET Image Process."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. 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