{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:47:43Z","timestamp":1743792463697,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"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":"crossref","award":["No.62076199"],"award-info":[{"award-number":["No.62076199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11760-023-02927-y","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T10:02:46Z","timestamp":1703844166000},"page":"2521-2531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Attention-based multi-scale recursive residual network for low-light image enhancement"],"prefix":"10.1007","volume":"18","author":[{"given":"Kaidi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanlin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiyang","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiwen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bangyong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"2927_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kang, L.: Combined image enhancement for recyclable waste object detection in low-light environment. In: 2022 6th international symposium on computer science and intelligent control (ISCSIC), pp. 265\u2013269 (2020)","DOI":"10.1109\/ISCSIC57216.2022.00062"},{"key":"2927_CR2","doi-asserted-by":"crossref","unstructured":"Ye, L., Ma, Z.: LLOD: a object detection method under low-light condition by feature enhancement and fusion. In: 2023 4th international seminar on artificial intelligence, networking and information technology (AINIT), pp. 659\u2013662 (2023)","DOI":"10.1109\/AINIT59027.2023.10212748"},{"key":"2927_CR3","doi-asserted-by":"publisher","first-page":"6296","DOI":"10.1109\/ACCESS.2020.3048366","volume":"9","author":"SW Cho","year":"2020","unstructured":"Cho, S.W., Baek, N.R., Koo, J.H., et al.: Modified perceptual cycle generative adversarial network-based image enhancement for improving accuracy of low light image segmentation. IEEE Access 9, 6296\u20136324 (2020)","journal-title":"IEEE Access"},{"key":"2927_CR4","doi-asserted-by":"crossref","unstructured":"Mamiya, K., Miyata, T.: Few-class learning for image-classification-aware denoising. In: IEEE international conference on image processing (ICIP), pp. 948\u2013952 (2020)","DOI":"10.1109\/ICIP40778.2020.9190795"},{"key":"2927_CR5","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.: LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognit. 61, 650\u2013662 (2017)","journal-title":"Pattern Recognit."},{"issue":"1","key":"2927_CR6","first-page":"4","volume":"220","author":"F Lv","year":"2018","unstructured":"Lv, F., Lu, F., Wu, J., et al.: MBLLEN: low-light image\/video enhancement using CNNs. BMVC. 220(1), 4 (2018)","journal-title":"BMVC."},{"key":"2927_CR7","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1780\u20131789 (2020)","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"2927_CR8","doi-asserted-by":"crossref","unstructured":"Lamba, M., Mitra, K.: Restoring extremely dark images in real time. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3487\u20133497 (2021)","DOI":"10.1109\/CVPR46437.2021.00349"},{"key":"2927_CR9","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., et al.: Enlightengan: deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340\u20132349 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"2927_CR10","unstructured":"Ren, J., et al.: Seeing through the noisy dark: toward real-world low-light image enhancement and denoising. arXiv:2210.00545. (2022)"},{"key":"2927_CR11","doi-asserted-by":"crossref","unstructured":"Fan, C. M., Liu, T. J., Liu, K. H.: Half wavelet attention on M-Net+ for low-light image enhancement. In: IEEE international conference on image processing (ICIP). (2022)","DOI":"10.1109\/ICIP46576.2022.9897503"},{"key":"2927_CR12","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., et al.: Learning to see in the dark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3291\u20133300 (2018)","DOI":"10.1109\/CVPR.2018.00347"},{"key":"2927_CR13","doi-asserted-by":"publisher","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","volume":"29","author":"LW Wang","year":"2020","unstructured":"Wang, L.W., Liu, Z.S., Siu, W.C., et al.: Lightening network for low-light image enhancement. IEEE Trans. Image Process. 29, 7984\u20137996 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"2927_CR14","first-page":"367","volume":"43","author":"MM Wang","year":"2022","unstructured":"Wang, M.M., Peng, D.L.: Retinex-ADNet: a low-light image enhancement system. J. Chin. Comput. 43(2), 367\u2013371 (2022)","journal-title":"J. Chin. Comput."},{"key":"2927_CR15","unstructured":"Zhang, Y., et al.: a fast and lightweight network for low-light image enhancement. arXiv:2304.02978 (2023)"},{"key":"2927_CR16","doi-asserted-by":"crossref","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G., et al.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st international conference on image processing, pp. 168\u2013172 (1994)","DOI":"10.1109\/ICIP.1994.413553"},{"key":"2927_CR17","doi-asserted-by":"crossref","unstructured":"Chu, X., Chen, L., Chen, C., et al.: Improving image restoration by revisiting global information aggregation. In: European conference on computer vision. Springer, Cham. pp. 53\u201371 (2022)","DOI":"10.1007\/978-3-031-20071-7_4"},{"key":"2927_CR18","doi-asserted-by":"crossref","unstructured":"Waqas Zamir, S., Arora, A., Khan, S., et al.: Multi-stage progressive image restoration. arXiv:2102.02808. (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"2927_CR19","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., et al.: Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"2927_CR20","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., et al.: Selective kernel networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"2927_CR21","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan., et al.: Learning enriched features for real image restoration and enhancement. In: Proceedings of the European conference on computer vision (ECCV), pp. 492\u2013511 (2020)","DOI":"10.1007\/978-3-030-58595-2_30"},{"key":"2927_CR22","unstructured":"Wei, C., Wang, W., Yang, W. et al.: Deep retinex decomposition for low-light enhancement. arXiv:1808.04560. (2018)"},{"issue":"2","key":"2927_CR23","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2016","unstructured":"Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982\u2013993 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"2927_CR24","doi-asserted-by":"publisher","first-page":"5372","DOI":"10.1109\/TIP.2013.2284059","volume":"22","author":"C Lee","year":"2013","unstructured":"Lee, C., Lee, C., Kim, C.S.: Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans. Image Process. 22(12), 5372\u20135384 (2013)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"2927_CR25","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1109\/TIP.2015.2442920","volume":"24","author":"K Ma","year":"2015","unstructured":"Ma, K., Zeng, K., Wang, Z.: Perceptual quality assessment for multi-exposure image fusion. IEEE Trans. Process. 24(11), 3345\u20133356 (2015)","journal-title":"IEEE Trans. Process."},{"key":"2927_CR26","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: A practical low-light image"},{"key":"2927_CR27","doi-asserted-by":"crossref","unstructured":"Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z:. Retinex-inspired Unrolling with cooperative prior architecture search for low-light image enhancement. In: Conference on computer vision and pattern recognition, pp. 10556\u201310565 (2021)","DOI":"10.1109\/CVPR46437.2021.01042"},{"key":"2927_CR28","unstructured":"Cui, Z., Li, K., Gu, L., et al.: You only need 90k parameters to adapt light: a light weight transformer for image enhancement and exposure correction. In: BMVC, pp. 21\u201324 (2022)"},{"key":"2927_CR29","doi-asserted-by":"crossref","unstructured":"Fu, Z., Yang, Y., Tu, X., Huang, Y., Ding, X., Ma, K.-K.: Learning a simple low-light image enhancer from paired low-light instances. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 22252\u201322261 (2023)","DOI":"10.1109\/CVPR52729.2023.02131"},{"key":"2927_CR30","doi-asserted-by":"crossref","unstructured":"Jie, H., Zuo, X., Gao, J., Liu, W., Hu, J., Cheng, S.: Llformer: An efficient and real-time lidar lane detection method based on transformer. In: Proceedings of the 2023 5th international conference on pattern recognition and intelligent systems, pp.18\u201323 (2023)","DOI":"10.1145\/3609703.3609707"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02927-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02927-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02927-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T01:35:48Z","timestamp":1730943348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02927-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2927"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02927-y","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2023,12,29]]},"assertion":[{"value":"18 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}