{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:52:22Z","timestamp":1782834742274,"version":"3.54.5"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732539","type":"print"},{"value":"9783031732546","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"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\/978-3-031-73254-6_20","type":"book-chapter","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T07:20:49Z","timestamp":1732692049000},"page":"341-357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["TTT-MIM: Test-Time Training with\u00a0Masked Image Modeling for\u00a0Denoising Distribution Shifts"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6530-1440","authenticated-orcid":false,"given":"Youssef","family":"Mansour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4559-4442","authenticated-orcid":false,"given":"Xuyang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5398-5183","authenticated-orcid":false,"given":"Serdar","family":"Caglar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2874-2984","authenticated-orcid":false,"given":"Reinhard","family":"Heckel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00182"},{"key":"20_CR2","unstructured":"Bao, H., Dong, L., Wei, F.: Beit: bert pre-training of image transformers. In: International Conference on Learning Representations (ICLR) (2022)"},{"key":"20_CR3","unstructured":"Batson, J., Royer, L.: Noise2Self: blind denoising by self-supervision. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Broaddus, C., Krull, A., Weigert, M., Schmidt, U., Myers, G.: Removing structured noise with self-supervised blind-spot networks. In: International Symposium on Biomedical Imaging (ISBI) (2020)","DOI":"10.1109\/ISBI45749.2020.9098336"},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Cao, H., et al.: Swin-Unet: unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision \u2013 ECCV 2022 Workshops. ECCV 2022. LNCS, vol. 13803, pp. 205\u2013218. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"20_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13667, pp. 17\u201333. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_2","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"20_CR7","unstructured":"Chen, M., et al.: Generative pretraining from pixels. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple Siamese representation learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"20_CR9","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 (ICML) (2022)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"20_CR11","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"20_CR12","unstructured":"Fahim, M.A.N.I., Boutellier, J.: SS-TTA: test-time adaption for self-supervised denoising methods. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2023)"},{"key":"20_CR13","unstructured":"Gunawan, A., Nugroho, M.A., Park, S.J.: Test-time adaptation for real image denoising via meta-transfer learning. In: arXiv preprint (2022)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Hu, Z., Yang, Z., Hu, X., Nevatia, R.: Simple: similar pseudo label exploitation for semi-supervised classification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01485"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Huang, T., Li, S., Jia, X., Lu, H., Liu, J.: Neighbor2neighbor: self-supervised denoising from single noisy images. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01454"},{"key":"20_CR17","unstructured":"Kundu, J.N., Venkat, N., Babu, R.V., et\u00a0al.: Universal source-free domain adaptation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)"},{"key":"20_CR18","unstructured":"Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: International Conference on Machine Learning (ICML) (2018)"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Mansour, Y., Heckel, R.: Zero-shot noise2noise: efficient image denoising without any data. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.01347"},{"key":"20_CR21","unstructured":"Mansour, Y., Lin, K., Heckel, R.: Image-to-image mlp-mixer for image reconstruction. In: arXiv preprint (2022)"},{"key":"20_CR22","unstructured":"Mohan, S., Vincent, J.L., Manzorro, R., Crozier, P., Fernandez-Granda, C., Simoncelli, E.: Adaptive denoising via gaintuning. In: Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.294"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Quan, Y., Chen, M., Pang, T., Ji, H.: Self2self with dropout: learning self-supervised denoising from single image. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00196"},{"key":"20_CR26","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":"20_CR27","doi-asserted-by":"crossref","unstructured":"Sidky, E.Y., Pan, X.: Report on the AAPM deep-learning sparse-view CT grand challenge. Med. Phys. (2022)","DOI":"10.1002\/mp.15489"},{"key":"20_CR28","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Tu, Z., et al.: Maxim: multi-axis MLP for image processing. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"20_CR30","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Vaksman, G., Elad, M., Milanfar, P.: LIDIA: lightweight learned image denoising with instance adaptation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)","DOI":"10.1109\/CVPRW50498.2020.00270"},{"key":"20_CR32","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general u-shaped transformer for image restoration. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Xie, Z., Geng, Z., Hu, J., Zhang, Z., Hu, H., Cao, Y.: Revealing the dark secrets of masked image modeling. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.01391"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Xie, Z., et al.: Simmim: a simple framework for masked image modeling. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"20_CR37","unstructured":"Xu, J., Li, H., Liang, Z., Zhang, D., Zhang, L.: Real-world noisy image denoising: a new benchmark. In: arXiv preprint (2018)"},{"key":"20_CR38","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Cycleisp: real image restoration via improved data synthesis. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00277"},{"key":"20_CR40","unstructured":"Zbontar, J., et al.: fastMRI: an open dataset and benchmarks for accelerated MRI. In: arXiv preprint (2018)"},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. (2017)","DOI":"10.1109\/TIP.2017.2662206"},{"key":"20_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A poisson-gaussian denoising dataset with real fluorescence microscopy images. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01198"}],"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\/978-3-031-73254-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T08:09:37Z","timestamp":1732694977000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73254-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"ISBN":["9783031732539","9783031732546"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73254-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]},"assertion":[{"value":"28 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"}}]}}