{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:41:26Z","timestamp":1775230886535,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732461","type":"print"},{"value":"9783031732478","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73247-8_10","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T12:02:20Z","timestamp":1730376140000},"page":"161-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intrinsic Single-Image HDR Reconstruction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-2803","authenticated-orcid":false,"given":"Sebastian","family":"Dille","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0800-1118","authenticated-orcid":false,"given":"Chris","family":"Careaga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1495-0491","authenticated-orcid":false,"given":"Ya\u011f\u0131z","family":"Aksoy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Banterle, F., Ledda, P., Debattista, K., Chalmers, A.: Inverse tone mapping. In: Proceedings of GRAPHITE (2006)","DOI":"10.1145\/1174429.1174489"},{"issue":"3\u201326","key":"10_CR2","first-page":"2","volume":"2","author":"H Barrow","year":"1978","unstructured":"Barrow, H., Tenenbaum, J., Hanson, A., Riseman, E.: Recovering intrinsic scene characteristics. Comput. vis. syst 2(3\u201326), 2 (1978)","journal-title":"Comput. vis. syst"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Careaga, C., Aksoy, Y.: Intrinsic image decomposition via ordinal shading. ACM Trans. Graph. 43(1) (2023)","DOI":"10.1145\/3630750"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Careaga, C., Miangoleh, S.M.H., Aksoy, Y.: Intrinsic harmonization for illumination-aware compositing. In: Proceedings of SIGGRAPH Asia (2023)","DOI":"10.1145\/3610548.3618178"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, S.K., et al.: CEVR: learning continuous exposure value representations for single-image HDR reconstruction. In: Proceedings of ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01194"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, Y., Zhang, Z., Qiao, Y., Dong, C.: HDRUnet: single image HDR reconstruction with denoising and dequantization. In: Proceedings of CVPR (2021)","DOI":"10.1109\/CVPRW53098.2021.00045"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Dang-Nguyen, D.T., Pasquini, C., Conotter, V., Boato, G.: RAISE: a raw images dataset for digital image forensics. In: Proceedings of MMSys (2015)","DOI":"10.1145\/2713168.2713194"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Debevec, P.: A median cut algorithm for light probe sampling. In: ACM SIGGRAPH 2005 Posters. ACM (2005)","DOI":"10.1145\/1186954.1187029"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. ACM Trans. Graph. (1997)","DOI":"10.1145\/258734.258884"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Eilertsen, G., Kronander, J., Denes, G., Mantiuk, R.K., Unger, J.: HDR image reconstruction from a single exposure using deep CNNs. ACM Trans. Graph. 36(6) (2017)","DOI":"10.1145\/3130800.3130816"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Endo, Y., Kanamori, Y., Mitani, J.: Deep reverse tone mapping. ACM Trans. Graph. 36(6) (2017)","DOI":"10.1145\/3130800.3130834"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1007\/s11263-021-01563-8","volume":"130","author":"E Garces","year":"2022","unstructured":"Garces, E., Rodriguez-Pardo, C., Casas, D., Lopez-Moreno, J.: A survey on intrinsic images: delving deep into lambert and beyond. Int. J. Comput. Vision 130, 836\u2013868 (2022)","journal-title":"Int. J. Comput. Vision"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Gilchrist, A., Jacobsen, A.: Perception of lightness and illumination in a world of one reflectance. Perception 13, 5\u201319 (1984)","DOI":"10.1068\/p130005"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Guo, C., Xiuhua, J.: LHDR: HDR reconstruction for legacy content using a lightweight DNN. In: Proceedings of ACCV (2022)","DOI":"10.1007\/978-3-031-26313-2_19"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Hanji, P., Mantiuk, R., Eilertsen, G., Hajisharif, S., Unger, J.: Comparison of single image HDR reconstruction methods\u2014the caveats of quality assessment. ACM Trans. Graph. (2022)","DOI":"10.1145\/3528233.3530729"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Kim, D., et al.: Large scale multi-illuminant (LSMI) dataset for developing white balance algorithm under mixed illumination. In: Proceedings of CVPR (2021)","DOI":"10.1109\/ICCV48922.2021.00241"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, S., Kang, S.J.: End-to-end differentiable learning to HDR image synthesis for multi-exposure images. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16272"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Kovaleski, R.P., Oliveira, M.M.: High-quality reverse tone mapping for a wide range of exposures. In: Proceedings of SIBGRAPI (2014)","DOI":"10.1109\/SIBGRAPI.2014.29"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Le, P.H., Le, Q., Nguyen, R., Hua, B.S.: Single-image HDR reconstruction by multi-exposure generation. In: Proceedings of WACV (2023)","DOI":"10.1109\/WACV56688.2023.00405"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Lee, S., An, G.H., Kang, S.J.: Deep recursive HDRI: inverse tone mapping using generative adversarial networks. In: Proceedings of ECCV (2018)","DOI":"10.1007\/978-3-030-01216-8_37"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: Learning intrinsic image decomposition from watching the world. In: Proceedings of CVPR (2018)","DOI":"10.1109\/CVPR.2018.00942"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Li, Z., Lu, M., Zhang, X., Feng, X., Asif, M.S., Ma, Z.: Efficient visual computing with camera raw snapshots. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3359326"},{"key":"10_CR23","unstructured":"Liu, L., et al.: On the variance of the adaptive learning rate and beyond. In: Proceedings of ICLR (2020)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y.L., et al.: Single-image HDR reconstruction by learning to reverse the camera pipeline. In: Proceedings of CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00172"},{"key":"10_CR25","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: Proceedings of ICLR (2017)"},{"key":"10_CR26","unstructured":"Mann, S., Picard, R.: On being \u201cundigital\" with digital cameras: extending dynamic range by combining differently exposed pictures. In: Proceedings of IS &T Annual Conference (1995)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Mantiuk, R.K., Azimi, M.: Pu21: a novel perceptually uniform encoding for adapting existing quality metrics for hdr. In: Proceedings of PCS (2021)","DOI":"10.1109\/PCS50896.2021.9477471"},{"key":"10_CR28","unstructured":"Mantiuk, R.K., Hammou, D., Hanji, P.: HDR-VDP-3: a multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content. arXiv preprint arXiv:2304.13625 (2023)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Maralan, S.S., Careaga, C., Aksoy, Y.: Computational flash photography through intrinsics. In: Proceedings of CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01598"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Marnerides, D., Bashford-Rogers, T., Hatchett, J., Debattista, K.: Expandnet: a deep convolutional neural network for high dynamic range expansion from low dynamic range content. Comput. Graph. Forum 37(2) (2018)","DOI":"10.1111\/cgf.13340"},{"key":"10_CR31","unstructured":"Murmann, L., Gharbi, M., Aittala, M., Durand, F.: A multi-illumination dataset of indoor object appearance. In: Proceedings of ICCV (2019)"},{"key":"10_CR32","unstructured":"Nemoto, H., Korshunov, P., Hanhart, P., Ebrahimi, T.: Visual attention in LDR and HDR images. In: Proceedings of VPQM (2015)"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(3) (2022)","DOI":"10.1109\/TPAMI.2020.3019967"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Rempel, A.G., et al.: LDR2HDR: on-the-fly reverse tone mapping of legacy video and photographs. ACM Trans. Graph. 26(3) (2007)","DOI":"10.1145\/1276377.1276426"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Roberts, M., et al.: Hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding. In: Proceedings of ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Santos, M.S., Ren, T.I., Kalantari, N.K.: Single image HDR reconstruction using a CNN with masked features and perceptual loss. ACM Trans. Graph. 39(4) (2020)","DOI":"10.1145\/3386569.3392403"},{"key":"10_CR37","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of ICML (2019)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Glowgan: unsupervised learning of HDR images from LDR images in the wild. In: Proceedings of ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00964"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"Xu, D., Doutre, C., Nasiopoulos, P.: Correction of clipped pixels in color images. IEEE Trans. Vis. Comput. Graph. 17(3) (2010)","DOI":"10.1109\/TVCG.2010.63"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, N., Ye, Y., Zhao, Y., Wang, R.: Revisiting the stack-based inverse tone mapping. In: Proceedings of CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00884"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, X., Brainard, D.H.: Estimation of saturated pixel values in digital color imaging. J. Optical Soc. Am. A 21(12) (2004)","DOI":"10.1364\/JOSAA.21.002301"},{"issue":"2","key":"10_CR42","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/cgf.142624","volume":"40","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Ayd\u0131n, T.: Deep HDR estimation with generative detail reconstruction. Comput. Graph. Gorum 40(2), 179\u2013190 (2021)","journal-title":"Comput. Graph. Gorum"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73247-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T12:07:00Z","timestamp":1730376420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73247-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031732461","9783031732478"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73247-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}