{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:19:35Z","timestamp":1783477175808,"version":"3.55.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Urban Info"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the development of modern urban systems, the use of cameras for video surveillance of urban environments has become an essential requirement. However, in nighttime scenes, especially under foggy weather conditions, the visual quality can significantly degrade due to uneven atmospheric illumination, leading to color distortion and errors in transmission rate estimation. In this study, we propose a novel nighttime dehazing algorithm that integrates Retinex theory with the light\u2013dark channel prior. Specifically, (1) we introduce a Retinex-based variational model to estimate global atmospheric light under low illumination conditions, effectively correcting color biases in the dehazed images; and (2) we combine the light and dark channel priors to refine the transmission rate estimation, resulting in an optimized dehazing framework. Extensive experiments on a real-world nighttime dataset demonstrate the method's applicability to varying fog densities and complex light source distributions. Experimental results show significant improvements in both color fidelity and detail preservation, enhancing the reliability of urban infrastructure video surveillance systems in challenging nighttime foggy environments.<\/jats:p>","DOI":"10.1007\/s44212-025-00087-7","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:03:52Z","timestamp":1770336232000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Nighttime image dehazing via Retinex theory and bright-dark channel priors"],"prefix":"10.1007","volume":"5","author":[{"given":"Xianglei","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"87_CR1","doi-asserted-by":"crossref","unstructured":"Ancuti, C. O., Ancuti, C., & Timofte, R. (2020). NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images, In Proceedings of the IEEE CVPR Workshops (NTIRE).","DOI":"10.1109\/CVPRW50498.2020.00230"},{"key":"87_CR2","doi-asserted-by":"publisher","first-page":"19709","DOI":"10.1038\/s41598-023-46693-w","volume":"13","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Wen, C., Liu, W., & He, W. (2023). A depth iterative illumination estimation network for low-light image enhancement based on Retinex theory. Scientific Reports, 13, 19709.","journal-title":"Scientific Reports"},{"issue":"11","key":"87_CR3","first-page":"2569","volume":"44","author":"S Fang","year":"2016","unstructured":"Fang, S., Zhao, Y., Li, X., et al. (2016). Nighttime image dehazing based on illumination estimation. Journal of Electronics, 44(11), 2569\u20132575.","journal-title":"Journal of Electronics"},{"issue":"5","key":"87_CR4","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.3788\/OPE.20182605.1191","volume":"26","author":"LI Geng-fei","year":"2018","unstructured":"Geng-fei, L. I., Gui-ju, L. I., Guang-liang, H. A. N., et al. (2018). Illumination compensation using retinex model based on bright channel prior[J]. Optics and Precision Engineering, 26(5), 1191\u20131200. https:\/\/doi.org\/10.3788\/OPE.20182605.1191","journal-title":"Optics and Precision Engineering"},{"issue":"9","key":"87_CR5","first-page":"2127","volume":"45","author":"F Guo","year":"2017","unstructured":"Guo, F., Zou, B., & Tang, J. (2017). Nighttime foggy image dehazing algorithm based on a multi-light source model. Journal of Electronics, 45(9), 2127\u20132134.","journal-title":"Journal of Electronics"},{"key":"87_CR6","doi-asserted-by":"crossref","unstructured":"Hao G., Du L., Xiao C., et al. (2019). A Hybrid L\u2082\u2013L\u209a Variational Model for Single Low-Light Image Enhancement with Bright Channel Prior. In Proc. IEEE International Conference on Image Processing (ICIP) (pp. 1925\u20131929).","DOI":"10.1109\/ICIP.2019.8803197"},{"issue":"12","key":"87_CR7","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","volume":"33","author":"K He","year":"2011","unstructured":"He, K., Sun, J., & Tang, X. (2011). Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341\u20132353. https:\/\/doi.org\/10.1109\/TPAMI.2010.168","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"87_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1364\/JOSA.61.000001","volume":"61","author":"EH Land","year":"1971","unstructured":"Land, E. H., & McCann, J. J. (1971). Lightness and retinex theory. Journal of the Optical Society of America, 61(1), 1\u201311.","journal-title":"Journal of the Optical Society of America"},{"key":"87_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R. T., & Brown, M. S.\u00a0(2015). Nighttime haze removal with glow and multiple light colors [C]. In Proceedings of the IEEE International Conference on Computer Vision Santiago (pp. 226\u2013234). 7\u201313.","DOI":"10.1109\/ICCV.2015.34"},{"issue":"13","key":"87_CR10","first-page":"57","volume":"28","author":"B Li","year":"2022","unstructured":"Li, B., & Liu, J. (2022). A review of image dehazing technology. Modern Computer, 28(13), 57\u201361.","journal-title":"Modern Computer"},{"key":"87_CR11","doi-asserted-by":"publisher","unstructured":"Liang, Y., Xiao, X., Zhou, Z., Li, L., & Quan, Y. (2026). Nighttime image dehazing via a physics-aware dynamic neural model with progressive contrastive regularization. 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