{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:09:19Z","timestamp":1743095359920,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930455"},{"type":"electronic","value":"9783030930462"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93046-2_38","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:01Z","timestamp":1641015001000},"page":"444-455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention Guided Retinex Architecture Search for\u00a0Robust Low-light Image Enhancement"],"prefix":"10.1007","author":[{"given":"Xiaoke","family":"Shang","sequence":"first","affiliation":[]},{"given":"Jingjie","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Long","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Shaomin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nai","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Bychkovsky, V., Paris, S., Chan, E., Durand, F.: Learning photographic global tonal adjustment with a database of input\/output image pairs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 97\u2013104 (2011)","DOI":"10.1109\/CVPR.2011.5995332"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Cai, B., Xu, X., Guo, K., Jia, K., Hu, B., Tao, D.: A joint intrinsic-extrinsic prior model for retinex. In: International Conference on Computer Vision, pp. 4000\u20134009 (2017)","DOI":"10.1109\/ICCV.2017.431"},{"key":"38_CR3","unstructured":"Chen, W., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: British Machine Vision Conference, pp. 1\u201312 (2018)"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Gool, L.V., Timofte, R.: Probabilistic regression for visual tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7183\u20137192 (2020)","DOI":"10.1109\/CVPR42600.2020.00721"},{"issue":"12","key":"38_CR5","doi-asserted-by":"publisher","first-page":"4965","DOI":"10.1109\/TIP.2015.2474701","volume":"24","author":"X Fu","year":"2015","unstructured":"Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965\u20134977 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2782\u20132790 (2016)","DOI":"10.1109\/CVPR.2016.304"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: Hit-detector: Hierarchical trinity architecture search for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11405\u201311414 (2020)","DOI":"10.1109\/CVPR42600.2020.01142"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Guo, J., Zhu, X., Zhao, C., Cao, D., Lei, Z., Li, S.Z.: Learning meta face recognition in unseen domains. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6163\u20136172 (2020)","DOI":"10.1109\/CVPR42600.2020.00620"},{"issue":"2","key":"38_CR9","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2017","unstructured":"Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982\u2013993 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR10","unstructured":"Jiang, Y., Gong, X., Liu, D., et al.: EnlightenGAN: deep light enhancement without paired supervision. arXiv preprint arXiv:1906.06972 (2019)"},{"issue":"7","key":"38_CR11","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/83.597272","volume":"6","author":"DJ Jobson","year":"1997","unstructured":"Jobson, D.J., Rahman, Z.U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965\u2013976 (1997)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"38_CR12","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/83.557356","volume":"6","author":"DJ Jobson","year":"1997","unstructured":"Jobson, D.J., Rahman, Z.U., Woodell, G.A.: Properties and performance of a center\/surround retinex. IEEE Trans. Image Process. 6(3), 451\u2013462 (1997)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: ECCV, pp. 694\u2013711 (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"38_CR14","first-page":"1","volume":"01","author":"C Li","year":"2021","unstructured":"Li, C., Guo, C., Chen, C.L.: Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Trans. Pattern Anal. Mach. Intell. 01, 1\u20131 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"38_CR15","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","volume":"104","author":"C Li","year":"2018","unstructured":"Li, C., Guo, J., Porikli, F., Pang, Y.: Lightennet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recogn. Lett. 104, 15\u201322 (2018)","journal-title":"Pattern Recogn. Lett."},{"issue":"6","key":"38_CR16","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","volume":"27","author":"M Li","year":"2018","unstructured":"Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828\u20132841 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"38_CR17","unstructured":"Liang, H., et al.: Darts+: improved differentiable architecture search with early stopping. arXiv preprint arXiv:1909.06035 (2019)"},{"key":"38_CR18","unstructured":"Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. In: International Conference on Learning Representations (2018)"},{"key":"38_CR19","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. arXiv preprint arXiv:2009.11551 (2020)","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Liu, R., Ma, L., Zhang, Y., Fan, X., Luo, Z.: Underexposed image correction via hybrid priors navigated deep propagation. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3052903"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, W., Shen, J., Tai, Y.W., Crandall, D.J., Hoi, S.C.: Learning video object segmentation from unlabeled videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8960\u20138970 (2020)","DOI":"10.1109\/CVPR42600.2020.00898"},{"key":"38_CR22","doi-asserted-by":"crossref","unstructured":"Ma, L., Liu, R., Zhang, J., Fan, X., Luo, Z.: Learning deep context-sensitive decomposition for low-light image enhancement. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3071245"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., et al.: Least squares generative adversarial networks. In: International Conference on Computer Vision, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"38_CR24","doi-asserted-by":"crossref","unstructured":"Rahman, Z.U., Jobson, D.J., Woodell, G.A.: Multi-scale retinex for color image enhancement. In: International Conference on Image Processing, pp. 1003\u20131006 (1996)","DOI":"10.1109\/ICIP.1996.560995"},{"key":"38_CR25","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6849\u20136857 (2019)","DOI":"10.1109\/CVPR.2019.00701"},{"key":"38_CR26","doi-asserted-by":"crossref","unstructured":"Xu, K., Yang, X., Yin, B., Lau, R.W.: Learning to restore low-light images via decomposition-and-enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2281\u20132290 (2020)","DOI":"10.1109\/CVPR42600.2020.00235"},{"key":"38_CR27","unstructured":"Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.J., Tian, Q., Xiong, H.: PC-darts: partial channel connections for memory-efficient differentiable architecture search. In: International Conference on Learning Representations (2020)"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., Liu, J.: From fidelity to perceptual quality: a semi-supervised approach for low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3063\u20133072 (2020)","DOI":"10.1109\/CVPR42600.2020.00313"},{"key":"38_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, Y., Chen, H., Shen, C.: Memory-efficient hierarchical neural architecture search for image denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3657\u20133666 (2020)","DOI":"10.1109\/CVPR42600.2020.00371"},{"key":"38_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: ACM MM, pp. 1632\u20131640 (2019)","DOI":"10.1145\/3343031.3350926"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93046-2_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T14:00:34Z","timestamp":1726408834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93046-2_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930455","9783030930462"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93046-2_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}