{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T14:45:18Z","timestamp":1767624318295,"version":"3.37.3"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["STI2030-Major Projects2021ZD0201900"],"award-info":[{"award-number":["STI2030-Major Projects2021ZD0201900"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12090052"],"award-info":[{"award-number":["12090052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["20720230017"],"award-info":[{"award-number":["20720230017"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wenzhou Institute, University of Chinese Academy of Sciences\u2019 startup fund","award":["WIUCASQD2023007"],"award-info":[{"award-number":["WIUCASQD2023007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01633-7","type":"journal-article","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T09:06:25Z","timestamp":1732611985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["UNet-Att: a self-supervised denoising and recovery model for two-photon microscopic image"],"prefix":"10.1007","volume":"11","author":[{"given":"Yuer","family":"Lu","sequence":"first","affiliation":[]},{"given":"Yongfa","family":"Ying","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jianwei","family":"Shuai","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9782-9354","authenticated-orcid":false,"given":"Jinjin","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"issue":"2","key":"1633_CR1","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/0165-0270(94)90189-9","volume":"54","author":"W Denk","year":"1994","unstructured":"Denk W, Delaney KR, Gelperin A, Kleinfeld D, Strowbridge BW, Tank DW, Yuste R (1994) Anatomical and functional imaging of neurons usinG 2-photon laser-scanning microscopy. J Neurosci Methods 54(2):151\u2013162","journal-title":"J Neurosci Methods"},{"issue":"12","key":"1633_CR2","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1038\/nmeth818","volume":"2","author":"F Helmchen","year":"2005","unstructured":"Helmchen F, Denk W (2005) Deep tissue two-photon microscopy. Nat Methods 2(12):932\u2013940","journal-title":"Nat Methods"},{"issue":"5","key":"1633_CR3","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.neuron.2012.02.011","volume":"73","author":"C Grienberger","year":"2012","unstructured":"Grienberger C, Konnerth A (2012) Imaging calcium in neurons. Neuron 73(5):862\u2013885","journal-title":"Neuron"},{"issue":"3","key":"1633_CR4","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1088\/0034-4885\/59\/3\/003","volume":"59","author":"RH Webb","year":"1996","unstructured":"Webb RH (1996) Confocal optical microscopy. Rep Prog Phys 59(3):427\u2013471","journal-title":"Rep Prog Phys"},{"issue":"1","key":"1633_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/jbio.201960062","volume":"13","author":"S Wang","year":"2020","unstructured":"Wang S, Lin B, Lin G, Lin R, Huang F, Liu W, Wang X, Liu X, Zhang Y, Wang F, Lin Y, Chen L, Chen J (2020) Automated label-free detection of injured neuron with deep learning by two-photon microscopy. J Biophotonics 13(1):1\u201313","journal-title":"J Biophotonics"},{"issue":"17","key":"1633_CR6","doi-asserted-by":"publisher","first-page":"8554","DOI":"10.1073\/pnas.1812995116","volume":"116","author":"S Soltanian-Zadeh","year":"2019","unstructured":"Soltanian-Zadeh S, Sahingur K, Blau S, Gong Y, Farsiu S (2019) Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. Proc Natl Acad Sci USA 116(17):8554\u20138563","journal-title":"Proc Natl Acad Sci USA"},{"key":"1633_CR7","doi-asserted-by":"crossref","unstructured":"Gu S, Zhang L, Zuo W, Feng X (2014) In Weighted nuclear norm minimization with application to image denoising, Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2862\u20132869","DOI":"10.1109\/CVPR.2014.366"},{"key":"1633_CR8","doi-asserted-by":"crossref","unstructured":"Chen Y, Pock T (2016) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. 39(6):1256\u20131272","DOI":"10.1109\/TPAMI.2016.2596743"},{"key":"1633_CR9","doi-asserted-by":"crossref","unstructured":"Zoran D, Weiss Y (2011) In From learning models of natural image patches to whole image restoration, international conference on computer vision, IEEE: 2011; pp 479\u2013486","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"1633_CR10","doi-asserted-by":"crossref","unstructured":"Buades A, Coll B, Morel J-M (2005) In A non-local algorithm for image denoising, IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), Ieee: 2005; pp 60\u201365","DOI":"10.1109\/CVPR.2005.38"},{"key":"1633_CR11","doi-asserted-by":"crossref","unstructured":"Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. 16(8):2080\u20132095","DOI":"10.1109\/TIP.2007.901238"},{"key":"1633_CR12","doi-asserted-by":"crossref","unstructured":"Razmjooy N, Ramezani M, Ghadimi N (2017) Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal. 19:1144\u20131156","DOI":"10.1007\/s40815-017-0305-2"},{"issue":"12","key":"1633_CR13","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","volume":"15","author":"M Elad","year":"2006","unstructured":"Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736\u20133745","journal-title":"IEEE Trans Image Process"},{"key":"1633_CR14","doi-asserted-by":"crossref","unstructured":"Meiniel W, Olivo-Marin J-C, Angelini E (2018) Denoising of microscopy images: a review of the state-of-the-art, and a new sparsity-based method. 27(8):3842\u20133856","DOI":"10.1109\/TIP.2018.2819821"},{"key":"1633_CR15","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhang J, Gao W, Bai F, Li N, Ghadimi N (2024) A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm. 90:105858","DOI":"10.1016\/j.bspc.2023.105858"},{"key":"1633_CR16","doi-asserted-by":"crossref","unstructured":"Liu H, Ghadimi N (2024) Hybrid convolutional neural network and flexible dwarf mongoose optimization algorithm for strong kidney stone diagnosis. 91:106024","DOI":"10.1016\/j.bspc.2024.106024"},{"key":"1633_CR17","doi-asserted-by":"crossref","unstructured":"Cai X, Li X, Razmjooy N, Ghadimi N (2021) Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm. Computational and Mathematical Methods in Medicine. 2021","DOI":"10.1155\/2021\/5595180"},{"key":"1633_CR18","doi-asserted-by":"crossref","unstructured":"Xu Z, Sheykhahmad FR, Ghadimi N, Razmjooy N (2020) Computer-aided diagnosis of skin cancer based on soft computing techniques. 15(1):860\u2013871","DOI":"10.1515\/med-2020-0131"},{"key":"1633_CR19","doi-asserted-by":"crossref","unstructured":"Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network\u2013world cup optimization algorithm for melanoma detection. 13(1):9\u201316","DOI":"10.1515\/med-2018-0002"},{"key":"1633_CR20","first-page":"1","volume":"6","author":"Q He","year":"2023","unstructured":"He Q, Zhong C-Q, Li X, Guo H, Li Y, Gao M, Yu R, Liu X, Zhang F, Guo D, Ye F, Guo T, Shuai J, Han J (2023) Dear-DIAXMBD: deep autoencoder enables deconvolution of Data-Independent Acquisition Proteomics. Res (Washington D C) 6:1\u201314","journal-title":"Res (Washington D C)"},{"issue":"10","key":"1633_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/brainsci11101274","volume":"11","author":"X Qian","year":"2021","unstructured":"Qian X, Qiu Y, He Q, Lu Y, Lin H, Xu F, Zhu F, Liu Z, Li X, Cao Y, Shuai J (2021) A review of methods for Sleep Arousal Detection using Polysomnographic signals. Brain Sci 11(10):1\u201327","journal-title":"Brain Sci"},{"issue":"1","key":"1633_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbad005","volume":"24","author":"H Hu","year":"2023","unstructured":"Hu H, Feng Z, Lin H, Zhao J, Zhang Y, Xu F, Chen L, Chen F, Ma Y, Su J, Zhao Q, Shuai J (2023) Modeling and analyzing single-cell multimodal data with deep parametric inference. Brief Bioinform 24(1):1\u201313","journal-title":"Brief Bioinform"},{"issue":"1","key":"1633_CR23","first-page":"1","volume":"24","author":"J Zhao","year":"2022","unstructured":"Zhao J, Sun J, Shuai SC, Zhao Q, Shuai J (2022) Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. Brief Bioinform 24(1):1\u20139","journal-title":"Brief Bioinform"},{"issue":"6","key":"1633_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbac463","volume":"23","author":"W Wang","year":"2022","unstructured":"Wang W, Zhang L, Sun J, Zhao Q, Shuai J (2022) Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field. Brief Bioinform 23(6):1\u20139","journal-title":"Brief Bioinform"},{"issue":"7553","key":"1633_CR25","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"1633_CR26","doi-asserted-by":"crossref","unstructured":"Chiang Y-W, Sullivan B (1989) In Multi-frame image restoration using a neural network, Proceedings of the 32nd Midwest Symposium on Circuits and Systems, IEEE: pp 744\u2013747","DOI":"10.1109\/MWSCAS.1989.101962"},{"key":"1633_CR27","unstructured":"Jain V, Seung S (2008) Natural image denoising with convolutional networks. 21"},{"key":"1633_CR28","unstructured":"Xie J, Xu L, Chen E (2012) Image denoising and inpainting with deep neural networks. 25"},{"key":"1633_CR29","doi-asserted-by":"crossref","unstructured":"Burger HC, Schuler CJ, Harmeling S (2012) In Image denoising: Can plain neural networks compete with BM3D? IEEE conference on computer vision and pattern recognition, IEEE: 2012; pp 2392\u20132399","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"1633_CR30","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"key":"1633_CR31","unstructured":"Mao X, Shen C, Yang Y-B (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in neural information processing systems 29"},{"key":"1633_CR32","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation, Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18, Springer: pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1633_CR33","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"Siddique N, Paheding S, Elkin CP, Devabhaktuni V (2021) U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9:82031\u201382057","journal-title":"IEEE Access"},{"issue":"6","key":"1633_CR34","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/TMI.2018.2823083","volume":"37","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yu H (2018) Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imaging 37(6):1370\u20131381","journal-title":"IEEE Trans Med Imaging"},{"key":"1633_CR35","doi-asserted-by":"crossref","unstructured":"Xiao T, Xu Y, Yang K, Zhang J, Peng Y, Zhang Z (2015) In The application of two-level attention models in deep convolutional neural network for fine-grained image classification, Proceedings of the IEEE conference on computer vision and pattern recognition, pp 842\u2013850","DOI":"10.1109\/CVPR.2015.7298685"},{"issue":"11","key":"1633_CR36","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1038\/s41592-021-01225-0","volume":"18","author":"X Li","year":"2021","unstructured":"Li X, Zhang G, Wu J, Zhang Y, Zhao Z, Lin X, Qiao H, Xie H, Wang H, Fang L (2021) Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat Methods 18(11):1395\u20131400","journal-title":"Nat Methods"},{"key":"1633_CR37","doi-asserted-by":"crossref","unstructured":"Ma C, Tan W, He R, Yan B (2024) Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration. Nat Methods 1\u201310","DOI":"10.1038\/s41592-024-02244-3"},{"key":"1633_CR38","unstructured":"Lehtinen J, Munkberg J, Hasselgren J, Laine S, Karras T, Aittala M, Aila T (2018) Noise2Noise: Learning image restoration without clean data. arXiv preprint arXiv:1803.04189"},{"key":"1633_CR39","unstructured":"Ioffe S, Szegedy CI (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift, International conference on machine learning, pmlr: pp 448\u2013456"},{"key":"1633_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) In Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1633_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, 26 (7), 3142\u20133155","DOI":"10.1109\/TIP.2017.2662206"},{"issue":"12","key":"1633_CR42","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1038\/s41592-018-0216-7","volume":"15","author":"M Weigert","year":"2018","unstructured":"Weigert M, Schmidt U, Boothe T, M\u00fcller A, Dibrov A, Jain A, Wilhelm B, Schmidt D, Broaddus C, Culley S (2018) Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15(12):1090\u20131097","journal-title":"Nat Methods"},{"key":"1633_CR43","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neunet.2020.01.026","volume":"125","author":"S Lee","year":"2020","unstructured":"Lee S, Negishi M, Urakubo H, Kasai H, Ishii S (2020) Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration. Neural Netw 125:92\u2013103","journal-title":"Neural Netw"},{"issue":"11","key":"1633_CR44","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"1633_CR45","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations"},{"key":"1633_CR46","doi-asserted-by":"crossref","unstructured":"Zhai X, Oliver A, Kolesnikov A, Beyer L (2019) In S4l: Self-supervised semi-supervised learning, Proceedings of the IEEE\/CVF international conference on computer vision, pp 1476\u20131485","DOI":"10.1109\/ICCV.2019.00156"},{"key":"1633_CR47","doi-asserted-by":"crossref","unstructured":"Jing L, Tian Y (2020) Self-supervised visual feature learning with deep neural networks: a survey. 43(11):4037\u20134058","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"1633_CR48","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) In Show, attend and tell: Neural image caption generation with visual attention, International conference on machine learning, PMLR: pp 2048\u20132057"},{"key":"1633_CR49","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun GI (2018) Squeeze-and-excitation networks, Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1633_CR50","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"issue":"11","key":"1633_CR51","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1038\/s42256-022-00547-8","volume":"4","author":"J Lequyer","year":"2022","unstructured":"Lequyer J, Philip R, Sharma A, Hsu W-H, Pelletier L (2022) A fast blind zero-shot denoiser. Nat Mach Intell 4(11):953\u2013963","journal-title":"Nat Mach Intell"},{"issue":"2","key":"1633_CR52","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1038\/s41587-022-01450-8","volume":"41","author":"X Li","year":"2023","unstructured":"Li X, Li Y, Zhou Y, Wu J, Zhao Z, Fan J, Deng F, Wu Z, Xiao G, He J (2023) Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit. Nat Biotechnol 41(2):282\u2013292","journal-title":"Nat Biotechnol"},{"issue":"12","key":"1633_CR53","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1038\/s43588-023-00568-2","volume":"3","author":"X Li","year":"2023","unstructured":"Li X, Hu X, Chen X, Fan J, Zhao Z, Wu J, Wang H, Dai Q (2023) Spatial redundancy transformer for self-supervised fluorescence image denoising. Nat Comput Sci 3(12):1067\u20131080","journal-title":"Nat Comput Sci"},{"issue":"1","key":"1633_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1103\/PhysRevA.76.013833","volume":"76","author":"M Bondani","year":"2007","unstructured":"Bondani M, Allevi A, Zambra G, Paris MGA, Andreoni A (2007) Sub-shot-noise photon-number correlation in a mesoscopic twin beam of light. Phys Rev A 76(1):1\u20135","journal-title":"Phys Rev A"},{"key":"1633_CR55","doi-asserted-by":"crossref","unstructured":"Boyat A, Joshi BK, Ieee In Image Denoising using Wavelet Transform and Median Filtering, 4th Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, INDIA, 2013 Nov 28\u201330; Ahmedabad, INDIA, 2013","DOI":"10.1109\/NUiCONE.2013.6780128"},{"issue":"106077","key":"1633_CR56","first-page":"1","volume":"140","author":"RF Laine","year":"2021","unstructured":"Laine RF, Jacquemet G, Krull A (2021) Imaging in focus: an introduction to denoising bioimages in the era of deep learning. Int J Biochem Cell Biol 140(106077):1\u20139","journal-title":"Int J Biochem Cell Biol"},{"issue":"6","key":"1633_CR57","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2020) UNet plus plus: redesigning skip connections to exploit Multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"1633_CR58","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1117\/1.1455011","volume":"11","author":"I Avcibas","year":"2002","unstructured":"Avcibas I, Sankur B, Sayood K (2002) Statistical evaluation of image quality measures. J Electron Imaging 11(2):206\u2013223","journal-title":"J Electron Imaging"},{"issue":"4","key":"1633_CR59","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1633_CR60","first-page":"306","volume":"14","author":"CE Shannon","year":"1997","unstructured":"Shannon CE (1997) The mathematical theory of communication (reprinted). M D Comput 14(4):306\u2013317","journal-title":"M D Comput"},{"issue":"29","key":"1633_CR61","doi-asserted-by":"publisher","first-page":"32982","DOI":"10.1021\/acsami.2c09546","volume":"14","author":"J Zhong","year":"2022","unstructured":"Zhong J, Song Z, Zhang L, Li X, He Q, Lu Y, Kariko S, Shaw P, Liu L, Ye F, Li L, Shuai J (2022) Assembly of Guanine Crystals as a low-polarizing Broadband Multilayer Reflector in a spider, Phoroncidia rubroargentea. ACS Appl Mater Interfaces 14(29):32982\u201332993","journal-title":"ACS Appl Mater Interfaces"},{"key":"1633_CR62","doi-asserted-by":"crossref","unstructured":"Li X, Zhang P, Yin Z, Xu F, Yang Z-H, Jin J, Qu J, Liu Z, Qi H, Yao C (2022) Caspase-1 and gasdermin D afford the optimal targets with distinct switching strategies in NLRP1b inflammasome-induced cell death. Research","DOI":"10.34133\/2022\/9838341"},{"issue":"6","key":"1633_CR63","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/TMI.2023.3236037","volume":"42","author":"J Wang","year":"2023","unstructured":"Wang J, Chen F, Ma Y, Wang L, Fei Z, Shuai J, Tang X, Zhou Q, Qin J (2023) XBound-Former: toward Cross-scale Boundary modeling in transformers. IEEE Trans Med Imaging 42(6):1735\u20131745","journal-title":"IEEE Trans Med Imaging"},{"key":"1633_CR64","unstructured":"Liu H, An J, Jia X, Lin S, Yao X, Gan L, Clerckx B, Yuen C, Bennis M, Debbah M (2024) Stacked Intelligent Metasurfaces for Wireless Sensing and Communication: Applications and Challenges"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01633-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01633-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01633-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:18:31Z","timestamp":1738268311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01633-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,26]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1633"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01633-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2024,11,26]]},"assertion":[{"value":"24 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"55"}}