{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T13:46:34Z","timestamp":1782481594630,"version":"3.54.5"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["SYSPG20241211173951079"],"award-info":[{"award-number":["SYSPG20241211173951079"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114222","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:25:40Z","timestamp":1781537140000},"page":"114222","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["LVAD: A realistic data synthesis strategy and coarse-to-fine framework for low-light video anomaly detection"],"prefix":"10.1016","volume":"180","author":[{"given":"Linxuan","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoquan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.patcog.2026.114222_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3729222","article-title":"Networking systems for video anomaly detection: A tutorial and survey","volume":"57","author":"Liu","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.patcog.2026.114222_b2","doi-asserted-by":"crossref","unstructured":"P. Wu, X. Zhou, G. Pang, Y. Sun, J. Liu, P. Wang, Y. Zhang, Open-vocabulary video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18297\u201318307.","DOI":"10.1109\/CVPR52733.2024.01732"},{"key":"10.1016\/j.patcog.2026.114222_b3","doi-asserted-by":"crossref","unstructured":"L. Zanella, W. Menapace, M. Mancini, Y. Wang, E. Ricci, Harnessing large language models for training-free video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18527\u201318536.","DOI":"10.1109\/CVPR52733.2024.01753"},{"key":"10.1016\/j.patcog.2026.114222_b4","doi-asserted-by":"crossref","unstructured":"H. Karim, K. Doshi, Y. Yilmaz, Real-time weakly supervised video anomaly detection, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 6848\u20136856.","DOI":"10.1109\/WACV57701.2024.00670"},{"key":"10.1016\/j.patcog.2026.114222_b5","doi-asserted-by":"crossref","unstructured":"N.-C. Ristea, F.-A. Croitoru, R.T. Ionescu, M. Popescu, F.S. Khan, M. Shah, Self-distilled masked auto-encoders are efficient video anomaly detectors, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 15984\u201315995.","DOI":"10.1109\/CVPR52733.2024.01513"},{"key":"10.1016\/j.patcog.2026.114222_b6","article-title":"Cross-modal attention fusion of RGB and skeleton for multimodal-driven video anomaly detection","volume":"179","author":"Boan","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114222_b7","doi-asserted-by":"crossref","unstructured":"F. Li, W. Liu, J. Chen, R. Zhang, Y. Wang, X. Zhong, Z. Wang, Anomize: Better open vocabulary video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 29203\u201329212.","DOI":"10.1109\/CVPR52734.2025.02719"},{"key":"10.1016\/j.patcog.2026.114222_b8","doi-asserted-by":"crossref","unstructured":"X. Wang, K. Ma, Q. Liu, Y. Zou, Y. Fu, Multi-object tracking in the dark, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 382\u2013392.","DOI":"10.1109\/CVPR52733.2024.00044"},{"key":"10.1016\/j.patcog.2026.114222_b9","doi-asserted-by":"crossref","unstructured":"C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in matlab, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2720\u20132727.","DOI":"10.1109\/ICCV.2013.338"},{"key":"10.1016\/j.patcog.2026.114222_b10","doi-asserted-by":"crossref","unstructured":"W. Liu, W. Luo, D. Lian, S. Gao, Future frame prediction for anomaly detection:a new baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6536\u20136545.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"10.1016\/j.patcog.2026.114222_b11","doi-asserted-by":"crossref","unstructured":"W. Sultani, C. Chen, M. Shah, Real-world anomaly detection in surveillance videos, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6479\u20136488.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"10.1016\/j.patcog.2026.114222_b12","doi-asserted-by":"crossref","unstructured":"C. Cao, Y. Lu, P. Wang, Y. Zhang, A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 20392\u201320401.","DOI":"10.1109\/CVPR52729.2023.01953"},{"key":"10.1016\/j.patcog.2026.114222_b13","article-title":"Self-supervised learning video anomaly detection based on time interval prediction and noise classification","volume":"158","author":"Liu","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114222_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112010","article-title":"Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention","volume":"170","author":"Lyu","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114222_b15","doi-asserted-by":"crossref","unstructured":"P. Wu, X. Zhou, G. Pang, L. Zhou, Q. Yan, P. Wang, Y. Zhang, VADCLIP: Adapting vision-language models for weakly supervised video anomaly detection, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2024, pp. 6074\u20136082.","DOI":"10.1609\/aaai.v38i6.28423"},{"issue":"12","key":"10.1016\/j.patcog.2026.114222_b16","doi-asserted-by":"crossref","first-page":"13642","DOI":"10.1109\/TCSVT.2024.3450734","article-title":"Batchnorm-based weakly supervised video anomaly detection","volume":"34","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.114222_b17","article-title":"DSCIL: Dynamic selected contrastive instance learning for weakly supervised video anomaly detection","volume":"158","author":"Zeng","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114222_b18","article-title":"MG-TVMF: Multi-grained text-video matching and fusing for weakly supervised video anomaly detection","volume":"172","author":"He","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114222_b19","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"322","article-title":"Not only look, but also listen: Learning multimodal violence detection under weak supervision","author":"Wu","year":"2020"},{"key":"10.1016\/j.patcog.2026.114222_b20","doi-asserted-by":"crossref","unstructured":"A. Acsintoae, A. Florescu, M.-I. Georgescu, T. Mare, P. Sumedrea, R.T. Ionescu, F.S. Khan, M. Shah, Ubnormal: New benchmark for supervised open-set video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20143\u201320153.","DOI":"10.1109\/CVPR52688.2022.01951"},{"issue":"4","key":"10.1016\/j.patcog.2026.114222_b21","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."},{"issue":"10","key":"10.1016\/j.patcog.2026.114222_b22","doi-asserted-by":"crossref","first-page":"4703","DOI":"10.1007\/s11263-024-02084-w","article-title":"Temporally consistent enhancement of low-light videos via spatial-temporal compatible learning","volume":"132","author":"Zhu","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2026.114222_b23","doi-asserted-by":"crossref","unstructured":"H. Li, J. Wang, J. Yuan, Y. Li, W. Weng, Y. Peng, Y. Zhang, Z. Xiong, X. Sun, Event-assisted low-light video object segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3250\u20133259.","DOI":"10.1109\/CVPR52733.2024.00313"},{"key":"10.1016\/j.patcog.2026.114222_b24","doi-asserted-by":"crossref","unstructured":"H. Jiang, B. Guan, Z. Liu, X. Liu, J. Yu, Z. Liu, S. Han, S. Liu, Learning to See in the Extremely Dark, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 7676\u20137685.","DOI":"10.1109\/ICCV51701.2025.00720"},{"issue":"12","key":"10.1016\/j.patcog.2026.114222_b25","doi-asserted-by":"crossref","first-page":"9303","DOI":"10.1109\/TPAMI.2024.3416731","article-title":"Advancing real-world image dehazing: Perspective, modules, and training","volume":"46","author":"Feng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114222_b26","doi-asserted-by":"crossref","unstructured":"S. Guo, Z. Yan, K. Zhang, W. Zuo, L. Zhang, Toward convolutional blind denoising of real photographs, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1712\u20131722.","DOI":"10.1109\/CVPR.2019.00181"},{"key":"10.1016\/j.patcog.2026.114222_b27","doi-asserted-by":"crossref","unstructured":"T. Brooks, B. Mildenhall, T. Xue, J. Chen, D. Sharlet, J.T. Barron, Unprocessing images for learned raw denoising, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11036\u201311045.","DOI":"10.1109\/CVPR.2019.01129"},{"issue":"10","key":"10.1016\/j.patcog.2026.114222_b28","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","article-title":"Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data","volume":"17","author":"Foi","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114222_b29","unstructured":"H. Li, X. Hu, H. Wang, Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios, in: Proceedings of the Thirteenth International Conference on Learning Representations, 2025, pp. 39525\u201339537."},{"key":"10.1016\/j.patcog.2026.114222_b30","doi-asserted-by":"crossref","unstructured":"W. Zou, H. Gao, W. Yang, T. Liu, Wave-mamba: Wavelet state space model for ultra-high-definition low-light image enhancement, in: Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 1534\u20131543.","DOI":"10.1145\/3664647.3681580"},{"key":"10.1016\/j.patcog.2026.114222_b31","series-title":"Proceedings of the 33rd ACM International Conference on Multimedia","first-page":"9549","article-title":"Towards perfection: Building inter-component mutual correction for retinex-based low-light image enhancement","author":"Cao","year":"2025"},{"key":"10.1016\/j.patcog.2026.114222_b32","doi-asserted-by":"crossref","unstructured":"L. Ma, T. Ma, R. Liu, X. Fan, Z. Luo, Toward fast, flexible, and robust low-light image enhancement, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5637\u20135646.","DOI":"10.1109\/CVPR52688.2022.00555"},{"issue":"2","key":"10.1016\/j.patcog.2026.114222_b33","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":"10.1016\/j.patcog.2026.114222_b34","doi-asserted-by":"crossref","unstructured":"Q. Li, L. Shen, S. Guo, Z. Lai, Wavelet integrated CNNs for noise-robust image classification, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7245\u20137254.","DOI":"10.1109\/CVPR42600.2020.00727"},{"issue":"8","key":"10.1016\/j.patcog.2026.114222_b35","doi-asserted-by":"crossref","first-page":"3918","DOI":"10.1109\/TIP.2018.2828329","article-title":"A fusion framework for camouflaged moving foreground detection in the wavelet domain","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114222_b36","unstructured":"T. Williams, R. Li, Wavelet pooling for convolutional neural networks, in: Proceedings of the International Conference on Learning Representations, 2018."},{"key":"10.1016\/j.patcog.2026.114222_b37","doi-asserted-by":"crossref","unstructured":"H. Wang, X. Wu, Z. Huang, E.P. Xing, High-frequency component helps explain the generalization of convolutional neural networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8684\u20138694.","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"10.1016\/j.patcog.2026.114222_b38","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TIP.2024.3372466","article-title":"Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection","volume":"33","author":"Cao","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114222_b39","series-title":"Proceedings of the IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"VadMamba: Exploring state space models for fast video anomaly detection","author":"Lyu","year":"2025"},{"key":"10.1016\/j.patcog.2026.114222_b40","doi-asserted-by":"crossref","unstructured":"C. Guo, C. Li, J. Guo, C.C. Loy, J. Hou, S. Kwong, R. Cong, Zero-reference deep curve estimation for low-light image enhancement, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1780\u20131789.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"10.1016\/j.patcog.2026.114222_b41","doi-asserted-by":"crossref","unstructured":"D. Feijoo, J.C. Benito, A. Garcia, M.V. Conde, DarkIR: Robust low-light image restoration, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 10879\u201310889.","DOI":"10.1109\/CVPR52734.2025.01016"},{"key":"10.1016\/j.patcog.2026.114222_b42","doi-asserted-by":"crossref","unstructured":"C. Park, M. Cho, M. Lee, S. Lee, FastAno: Fast Anomaly Detection via Spatiotemporal Patch Transformation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 2249\u20132259.","DOI":"10.1109\/WACV51458.2022.00197"},{"issue":"9","key":"10.1016\/j.patcog.2026.114222_b43","first-page":"4505","article-title":"A background-agnostic framework with adversarial training for abnormal event detection in video","volume":"44","author":"Georgescu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114222_b44","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Nie, C. Long, Q. Zhang, G. Li, A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 13588\u201313597.","DOI":"10.1109\/ICCV48922.2021.01333"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326011878?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326011878?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:53:06Z","timestamp":1782478386000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326011878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":44,"alternative-id":["S0031320326011878"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114222","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LVAD: A realistic data synthesis strategy and coarse-to-fine framework for low-light video anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114222","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114222"}}