{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:29:33Z","timestamp":1775244573833,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031726293","type":"print"},{"value":"9783031726309","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"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\/978-3-031-72630-9_20","type":"book-chapter","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T05:18:25Z","timestamp":1733289505000},"page":"341-358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Architecture-Agnostic Untrained Network Priors for\u00a0Image Reconstruction with\u00a0Frequency Regularization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2540-1295","authenticated-orcid":false,"given":"Yilin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2798-337X","authenticated-orcid":false,"given":"Yunkui","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6259-4932","authenticated-orcid":false,"given":"Jiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6183-2693","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1489-2102","authenticated-orcid":false,"given":"Pew-Thian","family":"Yap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Arican, M.E., Kara, O., Bredell, G., Konukoglu, E.: ISNAS-DIP: image-specific neural architecture search for deep image prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1960\u20131968 (2022)","DOI":"10.1109\/CVPR52688.2022.00200"},{"key":"20_CR2","unstructured":"Barbano, R., Antor\u00e1n, J., Leuschner, J., Hern\u00e1ndez-Lobato, J.M., Kereta, \u017d., Jin, B.: Fast and painless image reconstruction in deep image prior subspaces. arXiv preprint arXiv:2302.10279 (2023)"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Barbano, R., et al.: An educated warm start for deep image prior-based micro CT reconstruction. IEEE Trans. Comput. Imaging (2022)","DOI":"10.1109\/TCI.2022.3233188"},{"key":"20_CR4","unstructured":"Chakrabarty, P., Maji, S.: The spectral bias of the deep image prior. arXiv preprint arXiv:1912.08905 (2019)"},{"key":"20_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/978-3-030-58523-5_26","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y-C Chen","year":"2020","unstructured":"Chen, Y.-C., Gao, C., Robb, E., Huang, J.-B.: NAS-DIP: learning deep image prior with neural architecture search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 442\u2013459. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_26"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1109\/TCI.2021.3097596","volume":"7","author":"MZ Darestani","year":"2021","unstructured":"Darestani, M.Z., Heckel, R.: Accelerated MRI with un-trained neural networks. IEEE Trans. Comput. Imaging 7, 724\u2013733 (2021)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"20_CR7","unstructured":"Darestani, M.Z., Liu, J., Heckel, R.: Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing. In: International Conference on Machine Learning, pp. 4754\u20134776. PMLR (2022)"},{"key":"20_CR8","unstructured":"Woods, R.E., Gonzalez, R.C.: Digital Image Processing (2008)"},{"key":"20_CR9","unstructured":"Fridovich-Keil, S., Gontijo Lopes, R., Roelofs, R.: Spectral bias in practice: the role of function frequency in generalization. In: Advances in Neural Information Processing Systems, vol. 35, pp. 7368\u20137382 (2022)"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s10994-020-05929-w","volume":"110","author":"H Gouk","year":"2021","unstructured":"Gouk, H., Frank, E., Pfahringer, B., Cree, M.J.: Regularisation of neural networks by enforcing Lipschitz continuity. Mach. Learn. 110, 393\u2013416 (2021)","journal-title":"Mach. Learn."},{"issue":"3","key":"20_CR11","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1002\/jmri.24687","volume":"41","author":"MS Hansen","year":"2015","unstructured":"Hansen, M.S., Kellman, P.: Image reconstruction: an overview for clinicians. J. Magn. Reson. Imaging 41(3), 573\u2013585 (2015)","journal-title":"J. Magn. Reson. Imaging"},{"key":"20_CR12","unstructured":"Heckel, R., Hand, P.: Deep decoder: concise image representations from untrained non-convolutional networks. arXiv preprint arXiv:1810.03982 (2018)"},{"key":"20_CR13","unstructured":"Heckel, R., Soltanolkotabi, M.: Denoising and regularization via exploiting the structural bias of convolutional generators. In: International Conference on Representation Learning (2020)"},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.cag.2021.05.013","volume":"98","author":"K Ho","year":"2021","unstructured":"Ho, K., Gilbert, A., Jin, H., Collomosse, J.: Neural architecture search for deep image prior. Comput. Graph. 98, 188\u2013196 (2021)","journal-title":"Comput. Graph."},{"key":"20_CR15","unstructured":"Hoffman, J., Roberts, D.A., Yaida, S.: Robust learning with Jacobian regularization. arXiv preprint arXiv:1908.02729 (2019)"},{"issue":"1056","key":"20_CR16","doi-asserted-by":"publisher","first-page":"20150487","DOI":"10.1259\/bjr.20150487","volume":"88","author":"ON Jaspan","year":"2015","unstructured":"Jaspan, O.N., Fleysher, R., Lipton, M.L.: Compressed sensing MRI: a review of the clinical literature. Br. J. Radiol. 88(1056), 20150487 (2015)","journal-title":"Br. J. Radiol."},{"key":"20_CR17","unstructured":"Sawyer, A.M., et al.: Creation of fully sampled MR data repository for compressed sensing of the knee (2013)"},{"key":"20_CR18","unstructured":"Kaiser, J.: Nonrecursive digital filter design using the IO-sinh window function. Paper Presented at Symposium on Circuits and Systems, Institute of Electrical and Electronics. Proceedings 1974 IEEE International Symposium on Circuits & Systems, pp. 20\u201323 (1974)"},{"key":"20_CR19","unstructured":"Karras, T., et al.: Alias-free generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 852\u2013863 (2021)"},{"key":"20_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"1","key":"20_CR21","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1002\/mrm.27355","volume":"81","author":"F Knoll","year":"2019","unstructured":"Knoll, F., Hammernik, K., Kobler, E., Pock, T., Recht, M.P., Sodickson, D.K.: Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn. Reson. Med. 81(1), 116\u2013128 (2019)","journal-title":"Magn. Reson. Med."},{"issue":"6","key":"20_CR22","doi-asserted-by":"publisher","first-page":"3054","DOI":"10.1002\/mrm.28338","volume":"84","author":"F Knoll","year":"2020","unstructured":"Knoll, F., et al.: Advancing machine learning for MR image reconstruction with an open competition: overview of the 2019 fastMRI challenge. Magn. Reson. Med. 84(6), 3054\u20133070 (2020)","journal-title":"Magn. Reson. Med."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Knoll, F., et\u00a0al.: fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiol. Artif. Intell. 2(1), e190007 (2020)","DOI":"10.1148\/ryai.2020190007"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Korkmaz, Y., Dar, S.U., Yurt, M., \u00d6zbey, M., Cukur, T.: Unsupervised MRI reconstruction via zero-shot learned adversarial transformers. IEEE Trans. Med. Imaging (2022)","DOI":"10.1109\/TMI.2022.3147426"},{"issue":"5","key":"20_CR25","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1109\/TMI.2010.2100850","volume":"30","author":"SG Lingala","year":"2011","unstructured":"Lingala, S.G., Hu, Y., DiBella, E., Jacob, M.: Accelerated dynamic MRI exploiting sparsity and low-rank structure: KT SLR. IEEE Trans. Med. Imaging 30(5), 1042\u20131054 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Liu, D., Wang, J., Shan, Q., Smyl, D., Deng, J., Du, J.: DeepEIT: deep image prior enabled electrical impedance tomography. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3240565"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, Y., Xu, X., Kamilov, U.S.: Image restoration using total variation regularized deep image prior. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7715\u20137719. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682856"},{"key":"20_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-030-87231-1_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Chen, Y., Yap, P.-T.: Real-time mapping of tissue properties for magnetic resonance fingerprinting. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 161\u2013170. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_16"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, J., Pang, Y., Nie, D., Yap, P.T.: The devil is in the upsampling: architectural decisions made simpler for denoising with deep image prior. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12408\u201312417 (2023)","DOI":"10.1109\/ICCV51070.2023.01140"},{"issue":"6","key":"20_CR30","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"issue":"1","key":"20_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"20_CR32","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)"},{"issue":"12","key":"20_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac3a74","volume":"2021","author":"P Nakkiran","year":"2021","unstructured":"Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., Sutskever, I.: Deep double descent: where bigger models and more data hurt. J. Stat. Mech. Theory Exp. 2021(12), 124003 (2021)","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"20_CR34","unstructured":"Nittscher, M., Lameter, M., Barbano, R., Leuschner, J., Jin, B., Maass, P.: SVD-DIP: overcoming the overfitting problem in DIP-based CT reconstruction. arXiv preprint arXiv:2303.15748 (2023)"},{"key":"20_CR35","unstructured":"Novak, R., Bahri, Y., Abolafia, D.A., Pennington, J., Sohl-Dickstein, J.: Sensitivity and generalization in neural networks: an empirical study. arXiv preprint arXiv:1802.08760 (2018)"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Qayyum, A., Ilahi, I., Shamshad, F., Boussaid, F., Bennamoun, M., Qadir, J.: Untrained neural network priors for inverse imaging problems: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.36227\/techrxiv.14208215.v1"},{"key":"20_CR37","unstructured":"Rahaman, N., et al.: On the spectral bias of neural networks. In: International Conference on Machine Learning, pp. 5301\u20135310. PMLR (2019)"},{"key":"20_CR38","unstructured":"Rosca, M., Weber, T., Gretton, A., Mohamed, S.: A case for new neural network smoothness constraints. In: Zosa\u00a0Forde, J., Ruiz, F., Pradier, M.F., Schein, A. (eds.) Proceedings on \u201cI Can\u2019t Believe It\u2019s Not Better!\u201d. In: NeurIPS Workshops. Proceedings of Machine Learning Research, vol.\u00a0137, pp. 21\u201332. PMLR, 12 December 2020. https:\/\/proceedings.mlr.press\/v137\/rosca20a.html"},{"issue":"4","key":"20_CR39","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/s11263-021-01572-7","volume":"130","author":"Z Shi","year":"2022","unstructured":"Shi, Z., Mettes, P., Maji, S., Snoek, C.G.: On measuring and controlling the spectral bias of the deep image prior. Int. J. Comput. Vision 130(4), 885\u2013908 (2022)","journal-title":"Int. J. Comput. Vision"},{"key":"20_CR40","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7462\u20137473 (2020)"},{"key":"20_CR41","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7537\u20137547 (2020)"},{"key":"20_CR42","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van\u00a0Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"20_CR43","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446\u20139454 (2018)","DOI":"10.1109\/CVPR.2018.00984"},{"key":"20_CR44","unstructured":"Wang, H., Li, T., Zhuang, Z., Chen, T., Liang, H., Sun, J.: Early stopping for deep image prior. arXiv preprint arXiv:2112.06074 (2021)"},{"key":"20_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-030-36708-4_22","volume-title":"Neural Information Processing","author":"Z-QJ Xu","year":"2019","unstructured":"Xu, Z.-Q.J., Zhang, Y., Xiao, Y.: Training behavior of deep neural network in frequency domain. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11953, pp. 264\u2013274. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-36708-4_22"},{"key":"20_CR46","unstructured":"Yaman, B., Hosseini, S.A.H., Ak\u00e7akaya, M.: Zero-shot self-supervised learning for MRI reconstruction. In: International Conference on Learning Representations (2022)"},{"key":"20_CR47","doi-asserted-by":"crossref","unstructured":"Yang, J., Pavone, M., Wang, Y.: FreeNeRF: improving few-shot neural rendering with free frequency regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8254\u20138263 (2023)","DOI":"10.1109\/CVPR52729.2023.00798"},{"key":"20_CR48","doi-asserted-by":"crossref","unstructured":"Yu, T., et\u00a0al.: Validation and generalizability of self-supervised image reconstruction methods for undersampled MRI. arXiv preprint arXiv:2201.12535 (2022)","DOI":"10.59275\/j.melba.2022-6g33"}],"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\/978-3-031-72630-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T06:38:31Z","timestamp":1768199911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72630-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"ISBN":["9783031726293","9783031726309"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72630-9_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"5 December 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"}}]}}