{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:56:56Z","timestamp":1742961416288,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784868"},{"type":"electronic","value":"9789819784875"}],"license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"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\/978-981-97-8487-5_6","type":"book-chapter","created":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:03:53Z","timestamp":1730617433000},"page":"81-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PhaseNN: An Unsupervised and\u00a0Spatial-Frequency Integrated Network for\u00a0Phase Retrieval"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5461-7440","authenticated-orcid":false,"given":"Haining","family":"Hu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-0123","authenticated-orcid":false,"given":"Jie","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Xiaoguang","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Yuchen","family":"Hua","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"issue":"6","key":"6_CR1","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1364\/JOSAA.32.001160","volume":"32","author":"J Antonello","year":"2015","unstructured":"Antonello, J., Verhaegen, M.: Modal-based phase retrieval for adaptive optics. J. Opt. Soc. Am. A 32(6), 1160\u20131170 (2015)","journal-title":"J. Opt. Soc. Am. A"},{"issue":"1","key":"6_CR2","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3847\/1538-4365\/ac966c","volume":"263","author":"FJ Bail\u00e9n","year":"2022","unstructured":"Bail\u00e9n, F.J., Su\u00e1rez, D.O., Rodr\u00edguez, J.B., del Toro Iniesta, J.C.: Optimal defocus for phase diversity wave front retrieval. Astrophys. J. Suppl. Ser. 263(1), 8 (2022)","journal-title":"Astrophys. J. Suppl. Ser."},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s00332-012-9164-z","volume":"23","author":"B Birnir","year":"2013","unstructured":"Birnir, B.: The kolmogorov-obukhov statistical theory of turbulence. J. Nonlinear Sci. 23, 657\u2013688 (2013)","journal-title":"J. Nonlinear Sci."},{"doi-asserted-by":"crossref","unstructured":"Candes, E., Strohmer, T., Voroninski, V.: Phaselift: exact and stable signal recovery from magnitude measurements via convex programming. Commun. Pure Appl. Math. 66 (2013)","key":"6_CR4","DOI":"10.1002\/cpa.21432"},{"doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: unet-like pure transformer for medical image segmentation. In: Computer Vision\u2014ECCV 2022 Workshops, pp. 205\u2013218 (2023)","key":"6_CR5","DOI":"10.1007\/978-3-031-25066-8_9"},{"doi-asserted-by":"crossref","unstructured":"Conan, R., Correia, C.: Object-oriented Matlab adaptive optics toolbox. In: Adaptive Optics Systems IV. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol.\u00a09148 (2014)","key":"6_CR6","DOI":"10.1117\/12.2054470"},{"doi-asserted-by":"crossref","unstructured":"Couturier, R., Salomon, M., Zeid, E.A., Jaoude, C.A.: Using deep learning for object distance prediction in digital holography. In: 2021 International Conference on Computer, Control and Robotics (ICCCR), pp. 231\u2013235 (2021)","key":"6_CR7","DOI":"10.1109\/ICCCR49711.2021.9349275"},{"doi-asserted-by":"crossref","unstructured":"Dong, J., Valzania, L., Maillard, A., Pham, T.a., Gigan, S., Unser, M.: Phase retrieval: from computational imaging to machine learning: a tutorial. IEEE Signal Process. Mag. 40(1), 45\u201357 (2023)","key":"6_CR8","DOI":"10.1109\/MSP.2022.3219240"},{"unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)","key":"6_CR9"},{"key":"6_CR10","first-page":"237","volume":"35","author":"RW Gerchberg","year":"1972","unstructured":"Gerchberg, R.W.: A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 35, 237\u2013246 (1972)","journal-title":"Optik"},{"unstructured":"Goodman, J.W.: Introduction to Fourier optics. In: Goodman, J.W. (ed.) Introduction to Fourier Optics, 3rd ed, Roberts & Co. Publishers, Englewood, CO, vol 1 (2005)","key":"6_CR11"},{"doi-asserted-by":"crossref","unstructured":"Grafakos, L.: Classical Fourier Analysis (2014)","key":"6_CR12","DOI":"10.1007\/978-1-4939-1194-3"},{"doi-asserted-by":"crossref","unstructured":"Guo, H., Xu, Y., Li, Q., Du, S., He, D., Wang, Q., Huang, Y.: Improved machine learning approach for wavefront sensing. Sensors 19(16) (2019)","key":"6_CR13","DOI":"10.3390\/s19163533"},{"doi-asserted-by":"crossref","unstructured":"Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., Xu, C.: CMT: convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12175\u201312185 (2022)","key":"6_CR14","DOI":"10.1109\/CVPR52688.2022.01186"},{"doi-asserted-by":"crossref","unstructured":"Kong, L., Dong, J., Ge, J., Li, M., Pan, J.: Efficient frequency domain-based transformers for high-quality image deblurring. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5886\u20135895 (2023)","key":"6_CR15","DOI":"10.1109\/CVPR52729.2023.00570"},{"issue":"14","key":"6_CR16","doi-asserted-by":"publisher","first-page":"4168","DOI":"10.1364\/AO.455953","volume":"61","author":"Y Li","year":"2022","unstructured":"Li, Y., Yue, D., He, Y.: Prediction of wavefront distortion for wavefront sensorless adaptive optics based on deep learning. Appl. Opt. 61(14), 4168\u20134176 (2022)","journal-title":"Appl. Opt."},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1038\/22498","volume":"400","author":"JJ Miao","year":"1999","unstructured":"Miao, J.J., Charalambous, P.S., Kirz, J., Sayre, D.: Extending the methodology of x-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens. Nature 400, 342\u2013344 (1999)","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"Nair, V., Chatterjee, M., Tavakoli, N., Namin, A.S., Snoeyink, C.: Optimizing CNN using fast Fourier transformation for object recognition. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 234\u2013239 (2020)","key":"6_CR18","DOI":"10.1109\/ICMLA51294.2020.00046"},{"issue":"7","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1364\/JOSAA.9.001072","volume":"9","author":"RG Paxman","year":"1992","unstructured":"Paxman, R.G., Schulz, T.J., Fienup, J.R.: Joint estimation of object and aberrations by using phase diversity. J. Opt. Soc. Am. A 9(7), 1072\u20131085 (1992)","journal-title":"J. Opt. Soc. Am. A"},{"issue":"1","key":"6_CR20","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1146\/annurev-astro-091916-055320","volume":"56","author":"F Rigaut","year":"2018","unstructured":"Rigaut, F., Neichel, B.: Multiconjugate adaptive optics for astronomy. Annu. Rev. Astron. Astr. 56(1), 277\u2013314 (2018)","journal-title":"Annu. Rev. Astron. Astr."},{"doi-asserted-by":"crossref","unstructured":"Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., Lempitsky, V.: Resolution-robust large mask inpainting with Fourier convolutions. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3172\u20133182 (2022)","key":"6_CR21","DOI":"10.1109\/WACV51458.2022.00323"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","volume":"131","author":"C Tian","year":"2020","unstructured":"Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., Lin, C.W.: Deep learning on image denoising: an overview. Neural Netw. 131, 251\u2013275 (2020)","journal-title":"Neural Netw."},{"key":"6_CR23","doi-asserted-by":"publisher","DOI":"10.1201\/9781003140191","volume-title":"Principles of Adaptive Optics","author":"RK Tyson","year":"2022","unstructured":"Tyson, R.K., Frazier, B.W.: Principles of Adaptive Optics, 5th edn. CRC Press, Boca Raton London New York (2022)","edition":"5"},{"issue":"1","key":"6_CR24","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41377-020-0302-3","volume":"9","author":"F Wang","year":"2020","unstructured":"Wang, F., Bian, Y., Wang, H., Lyu, M., Pedrini, G., Osten, W., Barbastathis, G., Situ, G.: Phase imaging with an untrained neural network. Light Sci. Appl. 9(1), 77 (2020)","journal-title":"Light Sci. Appl."},{"doi-asserted-by":"crossref","unstructured":"Wu, Y., Guo, Y., Bao, H., Rao, C.: Sub-millisecond phase retrieval for phase-diversity wavefront sensor. Sensors 20(17) (2020)","key":"6_CR25","DOI":"10.3390\/s20174877"},{"doi-asserted-by":"crossref","unstructured":"Xu, Y., Guo, H., Wang, Z., He, D., Tan, Y., Huang, Y.: Self-supervised deep learning for improved image-based wave-front sensing. Photonics 9(3) (2022)","key":"6_CR26","DOI":"10.3390\/photonics9030165"},{"issue":"12","key":"6_CR27","doi-asserted-by":"publisher","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","volume":"21","author":"W Yang","year":"2019","unstructured":"Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q.: Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia 21(12), 3106\u20133121 (2019)","journal-title":"IEEE Trans. Multimedia"},{"issue":"5","key":"6_CR28","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TIP.2011.2176743","volume":"21","author":"G Yu","year":"2012","unstructured":"Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21(5), 2481\u20132499 (2012)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","key":"6_CR29","DOI":"10.1007\/978-3-030-01234-2_18"},{"doi-asserted-by":"crossref","unstructured":"Zhou, L., Song, J., Kim, J.S., Pei, X., Huang, C., Boyce, M., Mendonc\u0327a, L., Clare, D., Siebert, A., Allen, C.S., Liberti, E., Stuart, D., Pan, X., Nellist, P.D., Zhang, P., Kirkland, A.I., Wang, P.: Low-dose phase retrieval of biological specimens using cryo-electron ptychography. Nat. Commun. 11(1), 2773 (2020)","key":"6_CR30","DOI":"10.1038\/s41467-020-16391-6"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8487-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:05:49Z","timestamp":1730617549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8487-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"ISBN":["9789819784868","9789819784875"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8487-5_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"4 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}