{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T19:10:02Z","timestamp":1748200202615,"version":"3.41.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031918377","type":"print"},{"value":"9783031918384","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-91838-4_18","type":"book-chapter","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T18:33:54Z","timestamp":1748198034000},"page":"295-310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Solving Inverse Problem with Unspecified Forward Operator Using Diffusion Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4428-9216","authenticated-orcid":false,"given":"Jialing","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0912-9076","authenticated-orcid":false,"given":"Chongxuan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8270-8448","authenticated-orcid":false,"given":"Dequan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., Yoon, S.: Perception prioritized training of diffusion models. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01118"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN v2: diverse image synthesis for multiple domains. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chung, H., Kim, J., Kim, S., Ye, J.C.: Parallel diffusion models of operator and image for blind inverse problems. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00587"},{"key":"18_CR5","unstructured":"Chung, H., Kim, J., Mccann, M.T., Klasky, M.L., Ye, J.C.: Diffusion posterior sampling for general noisy inverse problems. In: ICLR (2023)"},{"key":"18_CR6","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: NeurIPS (2021)"},{"key":"18_CR7","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: NeurIPS (2017)"},{"key":"18_CR8","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS (2020)"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"18_CR10","unstructured":"Kawar, B., Elad, M., Ermon, S., Song, J.: Denoising diffusion restoration models. In: NeurIPS (2022)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better. In: CVPR (2019)","DOI":"10.1109\/ICCV.2019.00897"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Laroche, C., Almansa, A., Coupete, E.: Fast diffusion EM: a diffusion model for blind inverse problems with application to deconvolution. arXiv preprint arXiv:2309.00287 (2023)","DOI":"10.1109\/WACV57701.2024.00519"},{"key":"18_CR13","unstructured":"Murata, N., et al.: GibbsDDRM: a partially collapsed Gibbs sampler for solving blind inverse problems with denoising diffusion restoration. In: ICML (2023)"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Pan, J., Sun, D., Pfister, H., Yang, M.H.: Deblurring images via dark channel prior. TPAMI (2017)","DOI":"10.1109\/CVPR.2016.180"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Ren, D., Zhang, K., Wang, Q., Hu, Q., Zuo, W.: Neural blind deconvolution using deep priors. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00340"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et\u00a0al.: ImageNet large scale visual recognition challenge. IJCV (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: Image-to-image diffusion models. In: SIGGRAPH (2022)","DOI":"10.1145\/3528233.3530757"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. TPAMI (2022)","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"18_CR19","unstructured":"Song, J., Vahdat, A., Mardani, M., Kautz, J.: Pseudoinverse-guided diffusion models for inverse problems. In: ICLR (2023)"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Van\u00a0Dyk, D.A., Park, T.: Partially collapsed Gibbs samplers: theory and methods. JASA (2008)","DOI":"10.1198\/016214508000000409"},{"key":"18_CR21","unstructured":"Wang, Y., Yu, J., Zhang, J.: Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490 (2022)"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., Milanfar, P.: Deblurring via stochastic refinement. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"18_CR24","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"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91838-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T18:34:01Z","timestamp":1748198041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91838-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031918377","9783031918384"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91838-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"While our work leverages generative models to tackle inverse problems, we must be aware of dataset bias. Since our diffusion model is pre-trained on predefined data, any biases in that data could be reflected or even amplified by the model. Having been trained on biased data, the diffusion model is prone to projecting the given measurement towards these biases, potentially losing important or sensitive attributes of the input image in the process.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"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"}}]}}