{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:49:57Z","timestamp":1768258197524,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":68,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819985517","type":"print"},{"value":"9789819985524","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8552-4_11","type":"book-chapter","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:02:36Z","timestamp":1703660556000},"page":"130-145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["L$$^2$$DM: A Diffusion Model for\u00a0Low-Light Image Enhancement"],"prefix":"10.1007","author":[{"given":"Xingguo","family":"Lv","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingbo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyi","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"issue":"9","key":"11_CR1","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1109\/TIP.2009.2021548","volume":"18","author":"T Arici","year":"2009","unstructured":"Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 18(9), 1921\u20131935 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of IEEE International Conference on Image Processing, vol. 2, pp. 168\u2013172. IEEE (1994)","DOI":"10.1109\/ICIP.1994.413553"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3291\u20133300 (2018)","DOI":"10.1109\/CVPR.2018.00347"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Croitoru, F.A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: a survey. IEEE Trans. Pattern Anal. Mach. Intelli. (2023)","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"11_CR5","unstructured":"Cui, Z., et al.: Illumination adaptive transformer. arXiv preprint arXiv:2205.14871 (2022)"},{"key":"11_CR6","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Abandoning the Bayer-filter to see in the dark. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17431\u201317440 (2022)","DOI":"10.1109\/CVPR52688.2022.01691"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873\u201312883 (2021)","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"11_CR9","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: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3878\u20133882. IEEE (2022)","DOI":"10.1109\/ICIP46576.2022.9897503"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Fan, M., Wang, W., Yang, W., Liu, J.: Integrating semantic segmentation and retinex model for low-light image enhancement. In: Proceedings of the 28th ACM International Conference on Multimedia (ACMMM). pp. 2317\u20132325 (2020)","DOI":"10.1145\/3394171.3413757"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780\u20131789 (2020)","DOI":"10.1109\/CVPR42600.2020.00185"},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/s11263-022-01667-9","volume":"131","author":"X Guo","year":"2023","unstructured":"Guo, X., Hu, Q.: Low-light image enhancement via breaking down the darkness. Int. J. Comput. Vision 131(1), 48\u201366 (2023)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"11_CR13","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."},{"key":"11_CR14","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, N., Lin, J., Zhang, T., Zheng, H., Zhao, T.: Low-light image enhancement via stage-transformer-guided network. IEEE Trans. Circuits Syst. Video Technol. (2023)","DOI":"10.1109\/TCSVT.2023.3239511"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., et al.: EnlightenGAN: deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340\u20132349 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR18","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/978-3-031-19836-6_23","volume-title":"ECCV 2022, Part XXXVII","author":"Y Jin","year":"2022","unstructured":"Jin, Y., Yang, W., Tan, R.T.: Unsupervised night image enhancement: when layer decomposition meets light-effects suppression. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXXVII. LNCS, vol. 13697, pp. 404\u2013421. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19836-6_23"},{"key":"11_CR19","unstructured":"Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. arXiv preprint arXiv:2201.11793 (2022)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Kim, G., Kwon, D., Kwon, J.: Low-lightgan: low-light enhancement via advanced generative adversarial network with task-driven training. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2811\u20132815. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803328"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Kim, H., Choi, S.M., Kim, C.S., Koh, Y.J.: Representative color transform for image enhancement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4459\u20134468 (2021)","DOI":"10.1109\/ICCV48922.2021.00442"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Kosugi, S., Yamasaki, T.: Unpaired image enhancement featuring reinforcement-learning-controlled image editing software. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11296\u201311303 (2020)","DOI":"10.1609\/aaai.v34i07.6790"},{"issue":"12","key":"11_CR23","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."},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1109\/TMM.2020.3021243","volume":"23","author":"J Li","year":"2020","unstructured":"Li, J., Li, J., Fang, F., Li, F., Zhang, G.: Luminance-aware pyramid network for low-light image enhancement. IEEE Trans. Multimedia 23, 3153\u20133165 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"4272","DOI":"10.1109\/TMM.2020.3039361","volume":"23","author":"S Lim","year":"2020","unstructured":"Lim, S., Kim, W.: DSLR: deep stacked laplacian restorer for low-light image enhancement. IEEE Trans. Multimedia 23, 4272\u20134284 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"11_CR26","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10561\u201310570 (2021)","DOI":"10.1109\/CVPR46437.2021.01042"},{"key":"11_CR27","unstructured":"Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: DPM-solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps. arXiv preprint arXiv:2206.00927 (2022)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Ma, L., Ma, T., Liu, R., Fan, X., Luo, Z.: Toward fast, flexible, and robust low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5637\u20135646 (2022)","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"11_CR29","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Moran, S., Marza, P., McDonagh, S., Parisot, S., Slabaugh, G.: DeepLPF: deep local parametric filters for image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12826\u201312835 (2020)","DOI":"10.1109\/CVPR42600.2020.01284"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337\u20132346 (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"key":"11_CR32","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: International Conference on Machine Learning, pp. 1060\u20131069. PMLR (2016)"},{"issue":"4","key":"11_CR33","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1109\/TIP.2018.2876178","volume":"28","author":"W Ren","year":"2018","unstructured":"Ren, W., et al.: Deep video dehazing with semantic segmentation. IEEE Trans. Image Process. 28(4), 1895\u20131908 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"11_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: image-to-image diffusion models. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1\u201310 (2022)","DOI":"10.1145\/3528233.3530757"},{"key":"11_CR37","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256\u20132265. PMLR (2015)"},{"key":"11_CR38","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"11_CR39","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"11_CR40","unstructured":"Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR41","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR42","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-031-19797-0_20","volume-title":"ECCV 2022, Part XVIII","author":"H Wang","year":"2022","unstructured":"Wang, H., Xu, K., Lau, R.W.: Local color distributions prior for image enhancement. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XVIII. LNCS, vol. 13678, pp. 343\u2013359. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_20"},{"key":"11_CR43","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 (CVPR), pp. 6849\u20136857 (2019)","DOI":"10.1109\/CVPR.2019.00701"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Wang, T., Li, Y., Peng, J., Ma, Y., Wang, X., Song, F., Yan, Y.: Real-time image enhancer via learnable spatial-aware 3D lookup tables. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2471\u20132480 (2021)","DOI":"10.1109\/ICCV48922.2021.00247"},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.P., Kot, A.: Low-light image enhancement with normalizing flow. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2604\u20132612 (2022)","DOI":"10.1609\/aaai.v36i3.20162"},{"issue":"4","key":"11_CR46","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR47","unstructured":"Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 (2018)"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., Jiang, J.: URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901\u20135910 (2022)","DOI":"10.1109\/CVPR52688.2022.00581"},{"key":"11_CR49","doi-asserted-by":"crossref","unstructured":"Wu, X., Liu, X., Hiramatsu, K., Kashino, K.: Contrast-accumulated histogram equalization for image enhancement. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3190\u20133194. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296871"},{"key":"11_CR50","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":"11_CR51","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 (CVPR), pp. 2281\u20132290 (2020)","DOI":"10.1109\/CVPR42600.2020.00235"},{"key":"11_CR52","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1109\/LSP.2022.3233005","volume":"29","author":"W Xu","year":"2022","unstructured":"Xu, W., Dong, X., Ma, L., Teoh, A.B.J., Lin, Z.: Rawformer: an efficient vision transformer for low-light raw image enhancement. IEEE Signal Process. Lett. 29, 2677\u20132681 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"11_CR53","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, R., Fu, C.W., Jia, J.: SNR-aware low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17714\u201317724 (2022)","DOI":"10.1109\/CVPR52688.2022.01719"},{"key":"11_CR54","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, R., Lu, J.: Low-light image enhancement via structure modeling and guidance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9893\u20139903 (2023)","DOI":"10.1109\/CVPR52729.2023.00954"},{"key":"11_CR55","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":"11_CR56","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1109\/TIP.2021.3062184","volume":"30","author":"W Yang","year":"2021","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., Liu, J.: Band representation-based semi-supervised low-light image enhancement: bridging the gap between signal fidelity and perceptual quality. IEEE Trans. Image Process. 30, 3461\u20133473 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR57","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1109\/TIP.2021.3050850","volume":"30","author":"W Yang","year":"2021","unstructured":"Yang, W., Wang, W., Huang, H., Wang, S., Liu, J.: Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Trans. Image Process. 30, 2072\u20132086 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR58","unstructured":"Yu, J., et al.: Vector-quantized image modeling with improved VQGAN. arXiv preprint arXiv:2110.04627 (2021)"},{"issue":"7","key":"11_CR59","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.3390\/math11071657","volume":"11","author":"N Yuan","year":"2023","unstructured":"Yuan, N., et al.: Low-light image enhancement by combining transformer and convolutional neural network. Mathematics 11(7), 1657 (2023)","journal-title":"Mathematics"},{"key":"11_CR60","unstructured":"Yuan, Y., et al.: Learning to kindle the starlight. arXiv preprint arXiv:2211.09206 (2022)"},{"key":"11_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1007\/978-3-030-58595-2_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"SW Zamir","year":"2020","unstructured":"Zamir, S.W., et al.: Learning enriched features for real image restoration and enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 492\u2013511. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58595-2_30"},{"issue":"4","key":"11_CR62","first-page":"2058","volume":"44","author":"H Zeng","year":"2020","unstructured":"Zeng, H., Cai, J., Li, L., Cao, Z., Zhang, L.: Learning image-adaptive 3D lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell. 44(4), 2058\u20132073 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"11_CR64","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.: Beyond brightening low-light images. Int. J. Comput. Vision 129, 1013\u20131037 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"11_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: Proceedings of the 27th ACM International Conference on Multimedia (ACMMM), pp. 1632\u20131640 (2019)","DOI":"10.1145\/3343031.3350926"},{"key":"11_CR66","doi-asserted-by":"crossref","unstructured":"Zhao, L., Lu, S.P., Chen, T., Yang, Z., Shamir, A.: Deep symmetric network for underexposed image enhancement with recurrent attentional learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12075\u201312084 (2021)","DOI":"10.1109\/ICCV48922.2021.01186"},{"key":"11_CR67","doi-asserted-by":"crossref","unstructured":"Zheng, C., Shi, D., Shi, W.: Adaptive unfolding total variation network for low-light image enhancement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4439\u20134448 (2021)","DOI":"10.1109\/ICCV48922.2021.00440"},{"key":"11_CR68","doi-asserted-by":"crossref","unstructured":"Zhu, M., Pan, P., Chen, W., Yang, Y.: EEMEFN: low-light image enhancement via edge-enhanced multi-exposure fusion network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13106\u201313113 (2020)","DOI":"10.1609\/aaai.v34i07.7013"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8552-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T22:09:58Z","timestamp":1730930998000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8552-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"ISBN":["9789819985517","9789819985524"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8552-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"28 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}