{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:27:49Z","timestamp":1782491269120,"version":"3.54.5"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171038"],"award-info":[{"award-number":["62171038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61827901"],"award-info":[{"award-number":["61827901"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61936011"],"award-info":[{"award-number":["61936011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62088101"],"award-info":[{"award-number":["62088101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11263-023-01808-8","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T14:02:18Z","timestamp":1685109738000},"page":"2198-2218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Instance Segmentation in the Dark"],"prefix":"10.1007","volume":"131","author":[{"given":"Linwei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-694X","authenticated-orcid":false,"given":"Ying","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Heide","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"issue":"1","key":"1808_CR1","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.jvcir.2018.01.012","volume":"51","author":"J Anaya","year":"2018","unstructured":"Anaya, J., & Barbu, A. (2018). Renoir: A dataset for real low-light image noise reduction. Journal of Visual Communication and Image Representation, 51(1), 144\u2013154.","journal-title":"Journal of Visual Communication and Image Representation"},{"key":"1808_CR2","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., & Lee, Y. J. (2019). Yolact: Real-time instance segmentation. In Proceedings of IEEE international conference on computer vision (pp. 9157\u20139166).","DOI":"10.1109\/ICCV.2019.00925"},{"key":"1808_CR3","doi-asserted-by":"crossref","unstructured":"Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., & Barron, J. T. (2019). Unprocessing images for learned raw denoising. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 11036\u201311045).","DOI":"10.1109\/CVPR.2019.01129"},{"key":"1808_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Do, M. N., & Koltun, V. (2019a). Seeing motion in the dark. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 3185\u20133194).","DOI":"10.1109\/ICCV.2019.00328"},{"key":"1808_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., & Koltun, V. (2018). Learning to see in the dark. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 3291\u20133300).","DOI":"10.1109\/CVPR.2018.00347"},{"key":"1808_CR6","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., & Yan, Y. (2020). Blendmask: Top-down meets bottom-up for instance segmentation. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 8573\u20138581).","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"1808_CR7","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., & Ouyang, W., et\u00a0al. (2019b). Hybrid task cascade for instance segmentation. In Proceedings of IEEE international conference on computer vision (pp. 4974\u20134983).","DOI":"10.1109\/CVPR.2019.00511"},{"issue":"2","key":"1808_CR8","doi-asserted-by":"publisher","first-page":"252","DOI":"10.3390\/rs13020252","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen, L., Fu, Y., You, S., & Liu, H. (2021). Efficient hybrid supervision for instance segmentation in aerial images. Remote Sensing, 13(2), 252.","journal-title":"Remote Sensing"},{"key":"1808_CR9","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2022.05.026","volume":"496","author":"L Chen","year":"2022","unstructured":"Chen, L., Fu, Y., You, S., & Liu, H. (2022). Hybrid supervised instance segmentation by learning label noise suppression. Neurocomputing, 496, 131\u2013146.","journal-title":"Neurocomputing"},{"key":"1808_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 1290\u20131299).","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"1808_CR11","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of IEEE international conference on computer vision (pp. 3213\u20133223).","DOI":"10.1109\/CVPR.2016.350"},{"key":"1808_CR12","doi-asserted-by":"crossref","unstructured":"Cui, Z., Qi, G. J., Gu, L., You, S., Zhang, Z., & Harada, T. (2021). Multitask aet with orthogonal tangent regularity for dark object detection. In Proceedings of IEEE international conference on computer vision (pp. 2553\u20132562).","DOI":"10.1109\/ICCV48922.2021.00255"},{"issue":"5","key":"1808_CR13","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1007\/s11263-019-01182-4","volume":"128","author":"D Dai","year":"2020","unstructured":"Dai, D., Sakaridis, C., Hecker, S., & Van Gool, L. (2020). Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding. International Journal of Computer Vision, 128(5), 1182\u20131204.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR14","doi-asserted-by":"crossref","unstructured":"Dai, D., & Van\u00a0Gool, L. (2018). Dark model adaptation: Semantic image segmentation from daytime to nighttime. In Proceedings of international conference on intelligent transportation systems (pp. 3819\u20133824).","DOI":"10.1109\/ITSC.2018.8569387"},{"key":"1808_CR15","doi-asserted-by":"crossref","unstructured":"Dang-Nguyen, D. T., Pasquini, C., Conotter, V., & Boato, G. (2015). Raise: A raw images dataset for digital image forensics. In Proceedings of the 6th ACM multimedia systems conference (pp. 219\u2013224).","DOI":"10.1145\/2713168.2713194"},{"key":"1808_CR16","doi-asserted-by":"crossref","unstructured":"De\u00a0Brabandere, B., Neven, D., & Van\u00a0Gool, L. (2017). Semantic instance segmentation for autonomous driving. In Proceedings of IEEE conference on computer vision and pattern recognition workshops (pp. 7\u20139).","DOI":"10.1109\/CVPRW.2017.66"},{"issue":"3","key":"1808_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446918","volume":"40","author":"S Diamond","year":"2021","unstructured":"Diamond, S., Sitzmann, V., Julca-Aguilar, F., Boyd, S., Wetzstein, G., & Heide, F. (2021). Dirty pixels: Towards end-to-end image processing and perception. ACM Transactions on Graphics, 40(3), 1\u201315.","journal-title":"ACM Transactions on Graphics"},{"key":"1808_CR18","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. (2021). Repvgg: Making vgg-style convnets great again. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 13733\u201313742).","DOI":"10.1109\/CVPR46437.2021.01352"},{"issue":"2","key":"1808_CR19","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The Pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303\u2013338.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR20","doi-asserted-by":"crossref","unstructured":"Fang, K., Bai, Y., Hinterstoisser, S., Savarese, S., & Kalakrishnan, M. (2018). Multi-task domain adaptation for deep learning of instance grasping from simulation. In Proceedings of IEEE international conference on robotics and automation (pp. 3516\u20133523).","DOI":"10.1109\/ICRA.2018.8461041"},{"issue":"10","key":"1808_CR21","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","volume":"17","author":"A Foi","year":"2008","unstructured":"Foi, A., Trimeche, M., Katkovnik, V., & Egiazarian, K. (2008). Practical Poissonian\u2013Gaussian noise modeling and fitting for single-image raw-data. IEEE Transactions on Image Processing, 17(10), 1737\u20131754.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1808_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108010","volume":"240","author":"Y Fu","year":"2022","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. Knowledge-Based Systems, 240, 108010.","journal-title":"Knowledge-Based Systems"},{"issue":"7","key":"1808_CR23","first-page":"3404","volume":"44","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Zhang, T., Wang, L., & Huang, H. (2021). Coded hyperspectral image reconstruction using deep external and internal learning. IEEE Transactions Pattern Analysis and Machine Intelligence, 44(7), 3404\u20133420.","journal-title":"IEEE Transactions Pattern Analysis and Machine Intelligence"},{"key":"1808_CR24","doi-asserted-by":"crossref","unstructured":"Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2414\u20132423).","DOI":"10.1109\/CVPR.2016.265"},{"key":"1808_CR25","doi-asserted-by":"crossref","unstructured":"Gnanasambandam, A., & Chan, S. H. (2020). Image classification in the dark using quanta image sensors. In Proceedings of European conference on computer vision (pp. 484\u2013501).","DOI":"10.1007\/978-3-030-58598-3_29"},{"key":"1808_CR26","unstructured":"Gonzalez, R. C., & Woods, R. E., et\u00a0al. (2002). Digital image processing."},{"key":"1808_CR27","doi-asserted-by":"crossref","unstructured":"Gu, S., Li, Y., Gool, L. V., & Timofte, R. (2019). Self-guided network for fast image denoising. In Proceedings of IEEE international conference on computer vision (pp. 2511\u20132520).","DOI":"10.1109\/ICCV.2019.00260"},{"key":"1808_CR28","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 1780\u20131789).","DOI":"10.1109\/CVPR42600.2020.00185"},{"issue":"3","key":"1808_CR29","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1007\/s11263-010-0371-5","volume":"92","author":"J Hahn","year":"2011","unstructured":"Hahn, J., Tai, X. C., Borok, S., & Bruckstein, A. M. (2011). Orientation-matching minimization for image denoising and inpainting. International Journal of Computer Vision, 92(3), 308\u2013324.","journal-title":"International Journal of Computer Vision"},{"issue":"2","key":"1808_CR30","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s11263-010-0330-1","volume":"92","author":"MR Hajiaboli","year":"2011","unstructured":"Hajiaboli, M. R. (2011). An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), 177\u2013191.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of IEEE international conference on computer vision (pp. 2961\u20132969).","DOI":"10.1109\/ICCV.2017.322"},{"key":"1808_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"1808_CR33","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2(7)"},{"key":"1808_CR34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 7132\u20137141).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1808_CR35","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., & Wang, X. (2019). Mask scoring r-cnn. In Proceedings of IEEE international conference on computer vision (pp. 6409\u20136418).","DOI":"10.1109\/CVPR.2019.00657"},{"issue":"1","key":"1808_CR36","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021). Enlightengan: Deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30(1), 2340\u20132349.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1808_CR37","doi-asserted-by":"crossref","unstructured":"Julca-Aguilar, F., Taylor, J., Bijelic, M., Mannan, F., Tseng, E., & Heide, F. (2021). Gated3d: Monocular 3d object detection from temporal illumination cues. In Proceedings of IEEE international conference on computer vision (pp. 2938\u20132948).","DOI":"10.1109\/ICCV48922.2021.00293"},{"key":"1808_CR38","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., & Girshick, R. (2020). Pointrend: Image segmentation as rendering. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 9799\u20139808).","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"1808_CR39","doi-asserted-by":"crossref","unstructured":"Lamba, M., & Mitra, K. (2021). Restoring extremely dark images in real time. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 3487\u20133497).","DOI":"10.1109\/CVPR46437.2021.00349"},{"key":"1808_CR40","doi-asserted-by":"crossref","unstructured":"Lee, Y., & Park, J. (2019). Centermask: Real-time anchor-free instance segmentation. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 13906\u201313915).","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"1808_CR41","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017a). Feature pyramid networks for object detection. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 2117\u20132125).","DOI":"10.1109\/CVPR.2017.106"},{"key":"1808_CR42","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2017b). Focal loss for dense object detection. In Proceedings of IEEE international conference on computer vision (pp. 2980\u20132988).","DOI":"10.1109\/ICCV.2017.324"},{"key":"1808_CR43","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014a). Microsoft coco: Common objects in context. In Proceedings of European conference on computer vision (pp. 740\u2013755).","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1808_CR44","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In Proceedings of European conference on computer vision (pp. 740\u2013755).","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"1","key":"1808_CR45","first-page":"3695","volume":"29","author":"D Liu","year":"2020","unstructured":"Liu, D., Wen, B., Jiao, J., Liu, X., Wang, Z., & Huang, T. S. (2020). Connecting image denoising and high-level vision tasks via deep learning. IEEE TIP, 29(1), 3695\u20133706.","journal-title":"IEEE TIP"},{"issue":"4","key":"1808_CR46","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s11263-020-01418-8","volume":"129","author":"J Liu","year":"2021","unstructured":"Liu, J., Xu, D., Yang, W., Fan, M., & Huang, H. (2021). Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision, 129(4), 1153\u20131184.","journal-title":"International Journal of Computer Vision"},{"issue":"2","key":"1808_CR47","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietik\u00e4inen, M. (2020). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128(2), 261\u2013318.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR48","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qin, Z., Anwar, S., Ji, P., Kim, D., Caldwell, S., & Gedeon, T. (2021b). Invertible denoising network: A light solution for real noise removal. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 13365\u201313374).","DOI":"10.1109\/CVPR46437.2021.01316"},{"key":"1808_CR49","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021c). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of IEEE international conference on computer vision (pp. 10012\u201310022).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1808_CR50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C. Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A convnet for the 2020s. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 11976\u201311986).","DOI":"10.1109\/CVPR52688.2022.01167"},{"issue":"1","key":"1808_CR51","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.cviu.2018.10.010","volume":"178","author":"YP Loh","year":"2019","unstructured":"Loh, Y. P., & Chan, C. S. (2019). Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding, 178(1), 30\u201342.","journal-title":"Computer Vision and Image Understanding"},{"key":"1808_CR52","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","volume":"61","author":"KG Lore","year":"2017","unstructured":"Lore, K. G., Akintayo, A., & Sarkar, S. (2017). Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61, 650\u2013662.","journal-title":"Pattern Recognition"},{"issue":"7","key":"1808_CR53","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1007\/s11263-021-01466-8","volume":"129","author":"F Lv","year":"2021","unstructured":"Lv, F., Li, Y., & Lu, F. (2021). Attention guided low-light image enhancement with a large scale low-light simulation dataset. International Journal of Computer Vision, 129(7), 2175\u20132193.","journal-title":"International Journal of Computer Vision"},{"issue":"5","key":"1808_CR54","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1007\/s11263-021-01445-z","volume":"129","author":"R Mohan","year":"2021","unstructured":"Mohan, R., & Valada, A. (2021). Efficientps: Efficient panoptic segmentation. International Journal of Computer Vision, 129(5), 1551\u20131579.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR55","unstructured":"Morawski, I., Chen, Y. A., Lin, Y. S., & Hsu, W. H. (2021). Nod: Taking a closer look at detection under extreme low-light conditions with night object detection dataset. In Proceedings of the British machine vision conference (pp. 1\u201313)."},{"key":"1808_CR56","doi-asserted-by":"crossref","unstructured":"Plotz, T., & Roth, S. (2017). Benchmarking denoising algorithms with real photographs. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 1586\u20131595).","DOI":"10.1109\/CVPR.2017.294"},{"key":"1808_CR57","doi-asserted-by":"crossref","unstructured":"Punnappurath, A., Abuolaim, A., Abdelhamed, A., Levinshtein, A., & Brown, M. S. (2022). Day-to-night image synthesis for training nighttime neural isps. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 10769\u201310778).","DOI":"10.1109\/CVPR52688.2022.01050"},{"key":"1808_CR58","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 779\u2013788)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1808_CR59","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceedings of advances in neural information processing systems (pp. 91\u201399)."},{"key":"1808_CR60","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234\u2013241).","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"9","key":"1808_CR61","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","volume":"126","author":"C Sakaridis","year":"2018","unstructured":"Sakaridis, C., Dai, D., & Van Gool, L. (2018). Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9), 973\u2013992.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR62","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., & Van Gool, V. (2019). Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In Proceedings of IEEE international conference on computer vision (pp. 7374\u20137383).","DOI":"10.1109\/ICCV.2019.00747"},{"key":"1808_CR63","doi-asserted-by":"crossref","unstructured":"Sasagawa, Y., & Nagahara, H. (2020). Yolo in the dark-domain adaptation method for merging multiple models. In Proceedings of European conference on computer vision (pp. 345\u2013359).","DOI":"10.1007\/978-3-030-58589-1_21"},{"key":"1808_CR64","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. In Proceedings of international conference on learning representations (pp. 1\u201310)."},{"issue":"2","key":"1808_CR65","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s11263-006-0019-7","volume":"75","author":"S Tan","year":"2007","unstructured":"Tan, S., & Jiao, L. (2007). Multivariate statistical models for image denoising in the wavelet domain. International Journal of Computer Vision, 75(2), 209\u2013230.","journal-title":"International Journal of Computer Vision"},{"issue":"1","key":"1808_CR66","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.1109\/TIP.2021.3122004","volume":"30","author":"X Tan","year":"2021","unstructured":"Tan, X., Xu, K., Cao, Y., Zhang, Y., Ma, L., & Lau, R. W. (2021). Night-time scene parsing with a large real dataset. IEEE Transactions on Image Processing, 30(1), 9085\u20139098.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1808_CR67","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., & He, T. (2019). Fcos: Fully convolutional one-stage object detection. In Proceedings of IEEE international conference on computer vision (pp. 9627\u20139636).","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"7","key":"1808_CR68","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s11263-020-01303-4","volume":"128","author":"D Ulyanov","year":"2020","unstructured":"Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2020). Deep image prior. International Journal of Computer Vision, 128(7), 1867\u20131889.","journal-title":"International Journal of Computer Vision"},{"key":"1808_CR69","doi-asserted-by":"crossref","unstructured":"Wang, W., Wei, C., Yang, W., & Liu, J. (2018a). Gladnet: Low-light enhancement network with global awareness. In Proceedings of IEEE international conference on automatic face & gesture recognition (pp. 751\u2013755).","DOI":"10.1109\/FG.2018.00118"},{"key":"1808_CR70","doi-asserted-by":"crossref","unstructured":"Wang, W., Yang, W., & Liu, J. (2021). Hla-face: Joint high-low adaptation for low light face detection. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 16195\u201316204).","DOI":"10.1109\/CVPR46437.2021.01593"},{"key":"1808_CR71","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., & He, K. (2018b). Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7794\u20137803).","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1808_CR72","unstructured":"Wei, C., Wang, W., Yang, W., & Liu, J. (2018). Deep retinex decomposition for low-light enhancement. In Proceedings of the British machine vision conference (pp. 1\u201312)."},{"key":"1808_CR73","doi-asserted-by":"crossref","unstructured":"Wei, K., Fu, Y., Yang, J., & Huang, H. (2020). A physics-based noise formation model for extreme low-light raw denoising. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 2758\u20132767).","DOI":"10.1109\/CVPR42600.2020.00283"},{"issue":"1","key":"1808_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2021.3103114","volume":"1","author":"K Wei","year":"2021","unstructured":"Wei, K., Fu, Y., Zheng, Y., & Yang, J. (2021). Physics-based noise modeling for extreme low-light photography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(1), 1\u201317.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1808_CR75","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module. In Proceedings of European conference on computer vision (pp. 3\u201319).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1808_CR76","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Fu, Y., Zhang, L., & Huang, H. (2019). An effective network with convlstm for low-light image enhancement. In Pattern recognition and computer vision (pp. 221\u2013233).","DOI":"10.1007\/978-3-030-31723-2_19"},{"key":"1808_CR77","doi-asserted-by":"crossref","unstructured":"Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019). Feature denoising for improving adversarial robustness. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 501\u2013509).","DOI":"10.1109\/CVPR.2019.00059"},{"key":"1808_CR78","doi-asserted-by":"crossref","unstructured":"Xu, K., Yang, X., Yin, B., & Lau, R. W. (2020). Learning to restore low-light images via decomposition-and-enhancement. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 2281\u20132290).","DOI":"10.1109\/CVPR42600.2020.00235"},{"key":"1808_CR79","unstructured":"Yang, H., Kaixuan, W., Linwei, C., & Ying, F. (2021). Crafting object detection in very low light. In Proceedings of the British machine vision conference (pp. 1\u201315)."},{"key":"1808_CR80","doi-asserted-by":"crossref","unstructured":"Yang, W., Yuan, Y., Ren, W., Liu, J., Scheirer, W. J., Wang, Z., Zhang, T., Zhong, Q., Xie, D., Pu, S., et al. (2020). Advancing image understanding in poor visibility environments: A collective benchmark study. IEEE Transactions on Image Processing, 29(1), 5737\u20135752.","DOI":"10.1109\/TIP.2020.2981922"},{"key":"1808_CR81","doi-asserted-by":"crossref","unstructured":"Yang, W., Yuan, Y., Ren, W., Liu, J., Scheirer, W. J., Wang, Z., Zhang, T., Zhong, Q., Xie, D., Pu, S., et al. (2020). Advancing image understanding in poor visibility environments: A collective benchmark study. IEEE TIP, 29(1), 5737\u20135752.","DOI":"10.1109\/TIP.2020.2981922"},{"key":"1808_CR82","doi-asserted-by":"crossref","unstructured":"Zhang, F., Li, Y., You, S., & Fu, Y. (2021a). Learning temporal consistency for low light video enhancement from single images. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 4967\u20134976).","DOI":"10.1109\/CVPR46437.2021.00493"},{"issue":"11","key":"1808_CR83","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1007\/s11263-022-01660-2","volume":"130","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Fu, Y., & Zhang, J. (2022). Guided hyperspectral image denoising with realistic data. International Journal of Computer Vision, 130(11), 2885\u20132901.","journal-title":"International Journal of Computer Vision"},{"issue":"4","key":"1808_CR84","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s11263-020-01407-x","volume":"129","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Guo, X., Ma, J., Liu, W., & Zhang, J. (2021). Beyond brightening low-light images. International Journal of Computer Vision, 129(4), 1013\u20131037.","journal-title":"International Journal of Computer Vision"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01808-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01808-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01808-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T07:14:14Z","timestamp":1689578054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01808-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":84,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1808"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01808-8","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,26]]},"assertion":[{"value":"26 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}