{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:09:53Z","timestamp":1772694593258,"version":"3.50.1"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031314346","type":"print"},{"value":"9783031314353","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-31435-3_11","type":"book-chapter","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T07:03:12Z","timestamp":1682492592000},"page":"157-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RELIEF: Joint Low-Light Image Enhancement and\u00a0Super-Resolution with\u00a0Transformers"],"prefix":"10.1007","author":[{"given":"Andreas","family":"Aakerberg","sequence":"first","affiliation":[]},{"given":"Kamal","family":"Nasrollahi","sequence":"additional","affiliation":[]},{"given":"Thomas B.","family":"Moeslund","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"11_CR1","unstructured":"Aakerberg, A., Nasrollahi, K., Moeslund, T.B.: RELLISUR: a real low-light image super-resolution dataset. In: NeurIPS (2021)"},{"key":"11_CR2","unstructured":"Andreas Lugmayr et al.: Ntire 2020 challenge on real-world image super-resolution: methods and results. In: CVPRW (2020)"},{"key":"11_CR3","unstructured":"Ba, L.J., Kiros, J.R., Hinton, G.E.: Layer normalization (2016)"},{"key":"11_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-030-11021-5_21","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"Y Blau","year":"2019","unstructured":"Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., Zelnik-Manor, L.: The 2018 PIRM challenge on perceptual image super-resolution. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 334\u2013355. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_21"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Cai, J., Gu, S., Zhang, L.: Learning a deep single image contrast enhancer from multi-exposure images. TIP (2018)","DOI":"10.1109\/TIP.2018.2794218"},{"key":"11_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-58580-8_27","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Cai","year":"2020","unstructured":"Cai, Y., et al.: Learning delicate local representations for multi-person pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 455\u2013472. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_27"},{"key":"11_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Y., Kao, M., Chuang, Y.: Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00660"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"11_CR10","unstructured":"Chu, X., et al.: Conditional positional encodings for vision transformers (2021)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Coltuc, D., Bolon, P., Chassery, J.: Exact histogram specification. TIP (2006)","DOI":"10.1109\/TIP.2005.864170"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Ding, K., Ma, K., Wang, S., Simoncelli, E.P.: Image quality assessment: unifying structure and texture similarity (2020)","DOI":"10.1109\/TPAMI.2020.3045810"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. TPAMI 38, 295\u2013307 (2016)","journal-title":"TPAMI"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: CSWIN transformer: a general vision transformer backbone with cross-shaped windows (2021)","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"11_CR15","first-page":"4965","volume":"24","author":"X Fu","year":"2015","unstructured":"Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.S., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. TIP 24, 4965\u20134977 (2015)","journal-title":"TIP"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Guo, K., et al.: Deep illumination-enhanced face super-resolution network for low-light images. In: TOMM (2022)","DOI":"10.1145\/3495258"},{"key":"11_CR17","first-page":"982","volume":"26","author":"X Guo","year":"2017","unstructured":"Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. TIP 26, 982\u2013993 (2017)","journal-title":"TIP"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Han, T.Y., Kim, Y.J., Song, B.C.: Convolutional neural network-based infrared image super resolution under low light environment. In: EUSIPCO (2017)","DOI":"10.23919\/EUSIPCO.2017.8081318"},{"key":"11_CR19","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus) (2016)"},{"key":"11_CR20","first-page":"2340","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., et al.: Enlightengan: deep light enhancement without paired supervision. TIP 30, 2340\u20132349 (2021)","journal-title":"TIP"},{"key":"11_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"11_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-030-01219-9_7","volume-title":"Computer Vision \u2013 ECCV 2018","author":"TH Kim","year":"2018","unstructured":"Kim, T.H., Sajjadi, M.S.M., Hirsch, M., Sch\u00f6lkopf, B.: Spatio-temporal transformer network for video restoration. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 111\u2013127. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_7"},{"key":"11_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Klatzer, T., Hammernik, K., Kn\u00f6belreiter, P., Pock, T.: Learning joint demosaicing and denoising based on sequential energy minimization. In: ICCP (2016)","DOI":"10.1109\/ICCPHOT.2016.7492871"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00198"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: ICCVW (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Liang, Z., Zhang, D., Shao, J.: Jointly solving deblurring and super-resolution problems with dual supervised network. In: ICME (2019)","DOI":"10.1109\/ICME.2019.00141"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: CVPRW (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: a deep autoencoder approach to natural low-light image enhancement (2017)","DOI":"10.1016\/j.patcog.2016.06.008"},{"key":"11_CR33","unstructured":"Luo, Z., Huang, Y., Li, S., Wang, L., Tan, T.: Learning the degradation distribution for blind image super-resolution. In: CVPR (2022)"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Ma, C., Yan, B., Tan, W., Jiang, X.: Perception-oriented stereo image super-resolution. In: ACM MM (2021)","DOI":"10.1145\/3474085.3475408"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Ma, L., Liu, R., Wang, Y., Fan, X., Luo, Z.: Low-light image enhancement via self-reinforced retinex projection model. IEEE Trans. Multimedia (2022)","DOI":"10.1109\/TMM.2022.3162493"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Nasrollahi, K., Moeslund, T.B.: Super-resolution: A comprehensive survey. In: Mach. Vision Appl. (2014)","DOI":"10.1007\/s00138-014-0623-4"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Qin, Q., Yan, J., Wang, Q., Wang, X., Li, M., Wang, Y.: Etdnet: An efficient transformer deraining model. In: IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3108516"},{"key":"11_CR38","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_CR39","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S.M., Sch\u00f6lkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.481"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. In: NAACL-HLT (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"11_CR42","first-page":"889","volume":"9","author":"JA Stark","year":"2000","unstructured":"Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. TIP 9, 889\u2013896 (2000)","journal-title":"TIP"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B.A., Shlens, J.: Scaling local self-attention for parameter efficient visual backbones. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"11_CR44","unstructured":"Vaswani, A., et al.: In: NeurIPS (2017)"},{"key":"11_CR45","first-page":"3538","volume":"22","author":"S Wang","year":"2013","unstructured":"Wang, S., Zheng, J., Hu, H., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP 22, 3538\u20133548 (2013)","journal-title":"TIP"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: ESRGAN: enhanced super-resolution generative adversarial networks. In: ECCVW (2019)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"11_CR49","first-page":"600","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R.: Image quality assessment: from error visibility to structural similarity. TIP 13, 600\u2013612 (2004)","journal-title":"TIP"},{"key":"11_CR50","unstructured":"Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: BMVC (2018)"},{"key":"11_CR51","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: CVT: introducing convolutions to vision transformers (2021)","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"11_CR52","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers (2021)"},{"key":"11_CR53","unstructured":"Yang, J., et al.: Focal self-attention for local-global interactions in vision transformers (2021)"},{"key":"11_CR54","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.27.1.013026","volume":"27","author":"C Ying","year":"2018","unstructured":"Ying, C., Zhao, P., Li, Y.: Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm. J. Electron. Imaging 27, 013026 (2018)","journal-title":"J. Electron. Imaging"},{"key":"11_CR55","doi-asserted-by":"crossref","unstructured":"Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., Wu, W.: Incorporating convolution designs into visual transformers (2021)","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"11_CR56","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"},{"key":"11_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liang, J., Gool, L.V., Timofte, R.: Designing a practical degradation model for deep blind image super-resolution (2021)","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"11_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, P., et al.: Multi-scale vision longformer: a new vision transformer for high-resolution image encoding (2021)","DOI":"10.1109\/ICCV48922.2021.00299"},{"key":"11_CR59","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"11_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: ACM MM (2019)","DOI":"10.1145\/3343031.3350926"},{"key":"11_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"11_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"11_CR63","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"11_CR64","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-66415-2_31","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"R Zhou","year":"2020","unstructured":"Zhou, R., El Helou, M., Sage, D., Laroche, T., Seitz, A., S\u00fcsstrunk, S.: W2S: microscopy data with joint denoising and super-resolution for widefield to SIM mapping. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12535, pp. 474\u2013491. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66415-2_31"},{"key":"11_CR65","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"11_CR66","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31435-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T18:03:58Z","timestamp":1685383438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31435-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314346","9783031314353"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31435-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SCIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Scandinavian Conference on Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lapland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","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":"18 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/scia2023\/","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 3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","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":"67","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":"62% - 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","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","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)"}}]}}