{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:45:13Z","timestamp":1768567513796,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106214"],"award-info":[{"award-number":["62106214"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of Hebei Province","award":["F2019203195"],"award-info":[{"award-number":["F2019203195"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20333"],"award-info":[{"award-number":["U23A20333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01681-z","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:29:08Z","timestamp":1734604148000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["LDWLE: self-supervised driven low-light object detection framework"],"prefix":"10.1007","volume":"11","author":[{"given":"Xiaoyang","family":"shen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7241-5060","authenticated-orcid":false,"given":"Haibin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yaqian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wenming","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"1681_CR1","doi-asserted-by":"crossref","unstructured":"Everingham M, Eslami SMA, Gool LJ, Williams CKI, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111:98\u2013136","DOI":"10.1007\/s11263-014-0733-5"},{"key":"1681_CR2","volume-title":"Microsoft coco: common objects in context","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Zitnick CL (2014) Microsoft coco: common objects in context. Springer International Publishing"},{"key":"1681_CR3","doi-asserted-by":"crossref","unstructured":"Loh YP, Chan CS (2019) Getting to know low-light images with the exclusively dark dataset. Comput Vis Image Underst 178:30\u201342","DOI":"10.1016\/j.cviu.2018.10.010"},{"issue":"6","key":"1681_CR4","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.18517\/ijaseit.10.6.10948","volume":"10","author":"Y Harjoseputro","year":"2020","unstructured":"Harjoseputro Y, Yuda IP, Danukusumo KP (2020) Mobilenets: efficient convolutional neural network for identification of protected birds. Int J Adv Sci Eng Inform Technol 10(6):2290","journal-title":"Int J Adv Sci Eng Inform Technol"},{"issue":"6","key":"1681_CR5","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1681_CR6","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016). You only look once: unified, real-time object detection. In: Computer Vision & Pattern Recognition. IEEE","DOI":"10.1109\/CVPR.2016.91"},{"key":"1681_CR7","doi-asserted-by":"publisher","unstructured":"Berg AC, Fu CY, Szegedy C, Anguelov D, Erhan D, Reed S, et al (2015) SSD: Single Shot MultiBox Detector. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1681_CR8","unstructured":"Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement."},{"key":"1681_CR9","doi-asserted-by":"crossref","unstructured":"Huang J, Liu Y,\u00a0Zhao F, Yan K, Zhang J, Huang Y, Zhou M, Xiong Z (2022) Deep Fourier-based exposure correction network with spatial-frequency interaction. In: ECCV\u00a02022","DOI":"10.1007\/978-3-031-19800-7_10"},{"issue":"12","key":"1681_CR10","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, Fellow IEEE, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341\u20132353","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1681_CR11","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1681_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107404","volume":"106","author":"X Qin","year":"2020","unstructured":"Qin X, Zhang Z, Huang C, Dehghan M, Jagersand M (2020) U2-net: going deeper with nested u-structure for salient object detection. Pattern Recogn 106:107404","journal-title":"Pattern Recogn"},{"key":"1681_CR13","doi-asserted-by":"crossref","unstructured":"Fu Y, Hong Y, Chen L, You S (2022) Le-gan: unsupervised low-light image enhancement network using attention module and identity invariant loss. Knowl-Based Syst 240:108010","DOI":"10.1016\/j.knosys.2021.108010"},{"issue":"7","key":"1681_CR14","first-page":"13106","volume":"34","author":"M Zhu","year":"2020","unstructured":"Zhu M, Pan P, Chen W, Yang Y (2020) Eemefn: low-light image enhancement via edge-enhanced multi-exposure fusion network. Proc AAAI Conf Artif Intell 34(7):13106\u201313113","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1681_CR15","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang J, Guo X (2019) Kindling the darkness: a practical low-light image enhancer","DOI":"10.1145\/3343031.3350926"},{"key":"1681_CR16","doi-asserted-by":"crossref","unstructured":"Jiang Y, Gong X, Liu D, Cheng Y, Wang Z (2021) Enlightengan: deep light enhancement without paired supervision. IEEE Trans Image Process 30:2340\u20132349","DOI":"10.1109\/TIP.2021.3051462"},{"key":"1681_CR17","unstructured":"Dippel J, Vogler S, Hhne J. (2022) REPRESENTATION LEARNING. WO2022106302A1."},{"key":"1681_CR18","doi-asserted-by":"crossref","unstructured":"Deng X, Sun H, Lees A, Wu Y, Yu C (2022) Turl: table understanding through representation learning. ACM SIGMOD record 51(1):33\u201340","DOI":"10.1145\/3542700.3542709"},{"key":"1681_CR19","unstructured":"Karlsson R, Hayashi T, Fujii K, Carballo A, Ohtani K, Takeda, K (2021) Vice: improving dense representation learning by superpixelization and contrasting cluster assignment. arXiv e-prints"},{"key":"1681_CR20","doi-asserted-by":"crossref","unstructured":"Chen T, Guo J, Wu W (2022) Graph representation learning for popularity prediction problem: a survey","DOI":"10.1142\/S179383092230003X"},{"key":"1681_CR21","doi-asserted-by":"crossref","unstructured":"Yang Y, Long W, Li Y, Shi X, Gao L (2021) Image defogging based on amended dark channel prior and 4-directional l1 regularisation. IET Image Process 15(11):2454\u20132477","DOI":"10.1049\/ipr2.12233"},{"key":"1681_CR22","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang Z (2021) Image denoising method based on variable exponential fractional\u2010integer\u2010order total variation and tight frame sparse regularization. IET Image Process 15(1):101\u2013114","DOI":"10.1049\/ipr2.12010"},{"key":"1681_CR23","doi-asserted-by":"crossref","unstructured":"Stanberry L, Nandy R, Cordes D (2003) Cluster analysis of fmri data using dendrogram sharpening. Human Brain Mapp 20(4):201\u2013219","DOI":"10.1002\/hbm.10143"},{"key":"1681_CR24","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","volume":"61","author":"KG Lore","year":"2017","unstructured":"Lore KG, Akintayo A, Sarkar S (2017) Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650\u2013662","journal-title":"Pattern Recogn"},{"key":"1681_CR25","unstructured":"Lv F, Lu F, Wu J, et al (2018) MBLLEN: low-light image\/video enhancement using CNNs. In: BMVC. 2018, 220(1): 4"},{"key":"1681_CR26","doi-asserted-by":"crossref","unstructured":"Guo C, Li C, Guo J, Loy CC, Hou J, Kwong S, et al (2020) Zero-reference deep curve estimation for low-light image enhancement. In: 2020 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR). IEEE","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"1681_CR27","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu S, Wang X (2021) Learning continuous image representation with local implicit image function. In: Computer Vision and Pattern Recognition. IEEE","DOI":"10.1109\/CVPR46437.2021.00852"},{"key":"1681_CR28","doi-asserted-by":"crossref","unstructured":"Lee J, Jin KH (2022) Local texture estimator for implicit representation function. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1929\u20131938","DOI":"10.1109\/CVPR52688.2022.00197"},{"key":"1681_CR29","doi-asserted-by":"publisher","unstructured":"Reed AW, Kim H, Anirudh R, Mohan KA, Champley K, Kang J, et al (2021) Dynamic CT reconstruction from limited views with implicit neural representations and parametric motion fields. https:\/\/doi.org\/10.48550\/arXiv.2104.11745.","DOI":"10.48550\/arXiv.2104.11745"},{"key":"1681_CR30","unstructured":"Ziteng C,\u00a0Guo-Jun Q,\u00a0Gu L, et al (2022) Multitask AET with orthogonal tangent regularity for dark object detection.\u00a0CoRR\u2002abs\/2205.03346"},{"key":"1681_CR31","unstructured":"Chan CS, Loh YP (2018) Getting to know low-light images with the exclusively dark dataset. [2023\u201311\u201327]"},{"key":"1681_CR32","unstructured":"Andrew H, Mark S, Grace C, et al (2019) Searching for MobileNetV3. . In: Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"1681_CR33","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, et al (2009)ImageNet: A large-scale hierarchical image database, pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1681_CR34","doi-asserted-by":"publisher","unstructured":"Behley J, Garbade M, Milioto A, et al (2020) SemanticKITTI: a dataset for semantic scene understanding of LiDAR Sequences. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV).IEEE, 2020. https:\/\/doi.org\/10.1109\/ICCV.2019.00939.","DOI":"10.1109\/ICCV.2019.00939"},{"key":"1681_CR35","doi-asserted-by":"publisher","unstructured":"Wu W, et al (2022) URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In: 2022 IEEE\/CVF Conference on computer vision and pattern recognition (CVPR), 2022, https:\/\/doi.org\/10.1109\/cvpr52688.2022.00581.","DOI":"10.1109\/cvpr52688.2022.00581"},{"key":"1681_CR36","doi-asserted-by":"publisher","unstructured":"Dai D, Wang Y, Chen Y, et al (2015) Is image super-resolution helpful for other vision tasks? https:\/\/doi.org\/10.1109\/wacv.2016.7477613.","DOI":"10.1109\/wacv.2016.7477613"},{"key":"1681_CR37","doi-asserted-by":"crossref","unstructured":"Guo H, Lu T, Wu Y (2021) Dynamic low-light image enhancement for object detection via end-to-end training. In: 2020 25th International Conference on pattern recognition (ICPR). IEEE, 2021, pp 5611\u20135618","DOI":"10.1109\/ICPR48806.2021.9412802"},{"issue":"5","key":"1681_CR38","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.1109\/TNSE.2022.3151502","volume":"10","author":"Y Wu","year":"2022","unstructured":"Wu Y, Guo H, Chakraborty C et al (2022) Edge computing driven low-light image dynamic enhancement for object detection. IEEE Trans Netw Sci Eng 10(5):3086\u20133098","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"1681_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104521","volume":"150","author":"K Kou","year":"2024","unstructured":"Kou K, Yin X, Gao X et al (2024) Lightweight two-stage transformer for low-light image enhancement and object detection. Digital Signal Process 150:104521","journal-title":"Digital Signal Process"},{"key":"1681_CR40","first-page":"32","volume":"2023","author":"MM Mijwil","year":"2023","unstructured":"Mijwil MM, Doshi R, Hiran KK et al (2023) MobileNetV1-based deep learning model for accurate brain tumor classification. Mesop J Comput Sci 2023:32\u201341","journal-title":"Mesop J Comput Sci"},{"key":"1681_CR41","doi-asserted-by":"publisher","first-page":"17","DOI":"10.58496\/BJAI\/2023\/005","volume":"2023","author":"MA Mohammed","year":"2023","unstructured":"Mohammed MA, Ahmed MA, Hacimahmud AV (2023) Data-driven sustainability: leveraging big data and machine learning to build a greener future. Babylon J Artif Intell 2023:17\u201323","journal-title":"Babylon J Artif Intell"},{"issue":"13","key":"1681_CR42","doi-asserted-by":"publisher","first-page":"2972","DOI":"10.3390\/math11132972","volume":"11","author":"S Abdullah","year":"2023","unstructured":"Abdullah S, Almagrabi AO, Ali N (2023) A new method for commercial-scale water purification selection using linguistic neural networks. Mathematics 11(13):2972","journal-title":"Mathematics"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01681-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01681-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:18:17Z","timestamp":1738268297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01681-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1681"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01681-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"21 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that the research is conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"82"}}