{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:45:09Z","timestamp":1772801109296,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY17F010015"],"award-info":[{"award-number":["LY17F010015"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00371-020-01996-1","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T19:02:45Z","timestamp":1604689365000},"page":"2419-2431","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Low-dose CT lung images denoising based on multiscale parallel convolution neural network"],"prefix":"10.1007","volume":"37","author":[{"given":"Xiaoben","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8956-7684","authenticated-orcid":false,"given":"Yan","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"issue":"3","key":"1996_CR1","first-page":"783","volume":"51","author":"R Smith-Bindman","year":"2010","unstructured":"Smith-Bindman, R., Lipson, J., Marcus, R.: Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch. Intern. Med. 51(3), 783 (2010)","journal-title":"Arch. Intern. Med."},{"issue":"3","key":"1996_CR2","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1148\/radiology.175.3.2343122","volume":"175","author":"DP Naidich","year":"1990","unstructured":"Naidich, D.P., Marshall, C.H., Gribbin, C., et al.: Low-dose CT of the lungs: preliminary observations. Radiology 175(3), 729\u2013731 (1990)","journal-title":"Radiology"},{"issue":"1","key":"1996_CR3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1049\/ip-g-2.1992.0015","volume":"139","author":"CG Xie","year":"1992","unstructured":"Xie, C.G., Huang, S.M., Beck, M.S., et al.: Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors. IEE Proc. G (Circuits Dev. Syst.) 139(1), 89\u201398 (1992)","journal-title":"IEE Proc. G (Circuits Dev. Syst.)"},{"issue":"1","key":"1996_CR4","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neuroimage.2011.02.002","volume":"56","author":"RR Ram\u00edrez","year":"2011","unstructured":"Ram\u00edrez, R.R., Kopell, B.H., Butson, C.R., et al.: Spectral signal space projection algorithm for frequency domain MEG and EEG denoising, whitening, and source imaging. NeuroImage 56(1), 78\u201392 (2011)","journal-title":"NeuroImage"},{"key":"1996_CR5","doi-asserted-by":"crossref","unstructured":"Bhongade, S., Kourav, D., Rai, R.K., et al.: Review on image denoising based on contourlet domain using adaptive window algorithm. In: IEEE International Conference on Machine Intelligence and Research Advancement, Katra, India (2013)","DOI":"10.1109\/ICMIRA.2013.87"},{"issue":"6","key":"1996_CR6","first-page":"361","volume":"6","author":"PR Smith","year":"2001","unstructured":"Smith, P.R., Peters, T.M., Bates, R.H.T.: Image reconstruction from finite numbers of projections. J. Phys. A: Math. Nucl. Gen. 6(6), 361 (2001)","journal-title":"J. Phys. A: Math. Nucl. Gen."},{"issue":"11","key":"1996_CR7","doi-asserted-by":"publisher","first-page":"4911","DOI":"10.1118\/1.3232004","volume":"36","author":"A Manduca","year":"2009","unstructured":"Manduca, A., Yu, L., Trzasko, J.D., et al.: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med. Phys. 36(11), 4911\u20134919 (2009)","journal-title":"Med. Phys."},{"issue":"11","key":"1996_CR8","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1118\/1.598410","volume":"25","author":"J Hsieh","year":"1998","unstructured":"Hsieh, J.: Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise. Med. Phys. 25(11), 2139 (1998)","journal-title":"Med. Phys."},{"issue":"4","key":"1996_CR9","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1118\/1.1358303","volume":"28","author":"M Kachelriess","year":"2001","unstructured":"Kachelriess, M., Watzke, O., Kalender, W.A.: Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT. Med. Phys. 28(4), 475\u2013490 (2001)","journal-title":"Med. Phys."},{"issue":"1","key":"1996_CR10","doi-asserted-by":"publisher","first-page":"011908","DOI":"10.1118\/1.4851635","volume":"41","author":"Z Li","year":"2014","unstructured":"Li, Z., Yu, L., Trzasko, J.D., et al.: Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41(1), 011908 (2014)","journal-title":"Med. Phys."},{"issue":"1","key":"1996_CR11","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1166\/jmihi.2019.2552","volume":"9","author":"L Jia","year":"2019","unstructured":"Jia, L., Zhang, Q., Liu, Y., et al.: A two-step denoising method for low dose computed tomography image via morphological component analysis and non-local means. J. Med. Imaging Health Inf. 9(1), 140\u2013148 (2019)","journal-title":"J. Med. Imaging Health Inf."},{"key":"1996_CR12","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2018.01.015","volume":"284","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Zhang, Y.: Low-dose CT restoration via stacked sparse denoising autoencoders. Neurocomputing. 284, 80\u201389 (2018)","journal-title":"Neurocomputing."},{"issue":"6","key":"1996_CR13","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2017","unstructured":"Yang, Q., Yan, P., Zhang, Y., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348\u20131357 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7553","key":"1996_CR14","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.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"1996_CR15","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85\u2013117 (2015)","journal-title":"Neural Networks"},{"key":"1996_CR16","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS2012), Lake Tahoe, USA (2012)"},{"key":"1996_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations. Computer Vision & Pattern Recognition (CVPR2015), Boston, USA (2015)"},{"key":"1996_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Computer Vision & Pattern Recognition (CVPR2015), Boston, USA (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"7","key":"1996_CR19","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Pro. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Pro."},{"key":"1996_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision & Pattern Recognition (CVPR2016), Las Vegas, USA (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1996_CR21","unstructured":"Salimans, T., Kingma, D.P.: Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Neural Information Processing Systems (NIPS2016), Barcelona, Spain (2016)"},{"issue":"2","key":"1996_CR22","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/LSP.2017.2782270","volume":"25","author":"K Isogawa","year":"2017","unstructured":"Isogawa, K., Ida, T., Shiodera, T., et al.: Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Proc. Lett. 25(2), 224\u2013228 (2017)","journal-title":"IEEE Signal Proc. Lett."},{"issue":"4","key":"1996_CR23","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1049\/el.2018.6449","volume":"55","author":"Y Ding","year":"2018","unstructured":"Ding, Y., Hu, T.: Low-dose computed tomography scheme incorporating residual learning-based denoising with iterative reconstruction. Electron. Lett. 55(4), 174\u2013176 (2018)","journal-title":"Electron. Lett."},{"issue":"2","key":"1996_CR24","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen, H., Zhang, Y., Kalra, M.K., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(2), 2524\u20132535 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"1996_CR25","first-page":"704","volume":"75","author":"W Jifara","year":"2017","unstructured":"Jifara, W., Jiang, F., Rho, S., et al.: Medical image denoising using convolutional neural network: a residual learning approach. J. Supercomput. 75(6), 704\u2013718 (2017)","journal-title":"J. Supercomput."},{"issue":"11","key":"1996_CR26","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1049\/iet-ipr.2019.0241","volume":"13","author":"Y Jin","year":"2019","unstructured":"Jin, Y., Jiang, X.B., Wei, Z.K., et al.: Chest X-ray image denoising method based on deep convolution neural network. IET Image Proc. 13(11), 1970\u20131978 (2019)","journal-title":"IET Image Proc."},{"issue":"6","key":"1996_CR27","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2018","unstructured":"Yang, Q., Yan, P., Zhang, Y.: Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348\u20131357 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1996_CR28","doi-asserted-by":"crossref","unstructured":"Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Computer Vision & Pattern Recognition (CVPR2017), Hawaii, USA (2017)","DOI":"10.1109\/CVPR.2017.35"},{"issue":"6","key":"1996_CR29","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s00371-016-1234-z","volume":"32","author":"A Giachetti","year":"2016","unstructured":"Giachetti, A., Isaia, L., Garro, V.: Multiscale descriptors and metric learning for human body shape retrieval. Vis. Comput. 32(6), 693\u2013703 (2016)","journal-title":"Vis. Comput."},{"issue":"1","key":"1996_CR30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00371-016-1310-4","volume":"34","author":"H Zhang","year":"2018","unstructured":"Zhang, H., Liu, G.: Coupled-layer based visual tracking via adaptive kernelized correlation filters. Vis. Comput. 34(1), 41\u201354 (2018)","journal-title":"Vis. Comput."},{"key":"1996_CR31","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1007\/s00371-019-01775-7","volume":"36","author":"P Xi","year":"2020","unstructured":"Xi, P., Guan, H., Shu, C., et al.: An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis. Comput. 36, 1869\u20131882 (2020)","journal-title":"Vis. Comput."},{"key":"1996_CR32","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations. In: Computer Vision & Pattern Recognition (CVPR2016), Las Vegas, USA (2016)"},{"key":"1996_CR33","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1007\/s00371-019-01769-5","volume":"36","author":"H Li","year":"2020","unstructured":"Li, H., Zhang, S., Kong, W.: Bilateral counting network for single-image object counting. Vis. Comput. 36, 1693\u20131704 (2020)","journal-title":"Vis. Comput."},{"key":"1996_CR34","unstructured":"AAPM. (2017). Low Dose CT Grand Challenge. [Online]. Available: http:\/\/www.aapm.org\/GrandChallenge\/LowDoseCT\/"},{"issue":"5","key":"1996_CR35","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1364\/JOSAA.8.000801","volume":"8","author":"TJ Schulz","year":"1991","unstructured":"Schulz, T.J., Snyder, D.L.: Imaging a randomly moving object from quantum-limited data: applications to image recovery from second-and third-order autocorrelations. J. Opt. Soc. Am. A 8(5), 801\u2013807 (1991)","journal-title":"J. Opt. Soc. Am. A"},{"key":"1996_CR36","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Neural Information Processing Systems (NIPS2015), Montreal, Canada (2015)"},{"issue":"2","key":"1996_CR37","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1137\/040616024","volume":"4","author":"A Buades","year":"2005","unstructured":"Buades, A., Coll, B., Morel, J.-M.: A review of image denoising algorithms, with a new one. SIAM J. Multiscale Model. Simul. 4(2), 490\u2013530 (2005)","journal-title":"SIAM J. Multiscale Model. Simul."}],"updated-by":[{"DOI":"10.1007\/s00371-020-02039-5","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000}}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-020-01996-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-020-01996-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-020-01996-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T18:10:41Z","timestamp":1627755041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-020-01996-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,6]]},"references-count":37,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["1996"],"URL":"https:\/\/doi.org\/10.1007\/s00371-020-01996-1","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00371-020-02039-5","asserted-by":"object"}]},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,6]]},"assertion":[{"value":"12 October 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2020","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00371-020-02039-5","URL":"https:\/\/doi.org\/10.1007\/s00371-020-02039-5","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}