{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:18:11Z","timestamp":1761581891324,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T00:00:00Z","timestamp":1573257600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T00:00:00Z","timestamp":1573257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702157","41804118"],"award-info":[{"award-number":["61702157","41804118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"crossref","award":["QN2018085"],"award-info":[{"award-number":["QN2018085"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Innovation Capacity Improvement Project of Hebei Province","award":["199676146H"],"award-info":[{"award-number":["199676146H"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s11227-019-03066-3","type":"journal-article","created":{"date-parts":[[2019,11,9]],"date-time":"2019-11-09T20:02:40Z","timestamp":1573329760000},"page":"1005-1019","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Pulmonary nodule image super-resolution using multi-scale deep residual channel attention network with joint optimization"],"prefix":"10.1007","volume":"76","author":[{"given":"Yongjun","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhua","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zepei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juanping","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,9]]},"reference":[{"issue":"3","key":"3066_CR1","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1109\/TIP.2017.2651411","volume":"26","author":"J Choi","year":"2017","unstructured":"Choi J, Kim M (2017) Single image super-resolution using global regression based on multiple local linear mappings. IEEE Trans Image Process 26(3):1300\u20131314","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"3066_CR2","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861\u20132873","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"3066_CR3","doi-asserted-by":"publisher","first-page":"3467","DOI":"10.1109\/TIP.2012.2192127","volume":"21","author":"J Yang","year":"2012","unstructured":"Yang J, Wang Z, Lin Z, Cohen S, Huang TS (2012) Coupled dictionary training for image super-resolution. IEEE Trans Image Process 21(8):3467\u20133478","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"3066_CR4","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TII.2017.2653184","volume":"13","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Jiang W, Li F, Zhao M, Li B, Zhang L (2017) Structured latent label consistent dictionary learning for salient machine faults representation-based robust classification. IEEE Trans Ind Inform 13(2):644\u2013656","journal-title":"IEEE Trans Ind Inform"},{"issue":"4","key":"3066_CR5","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1109\/TIP.2017.2654163","volume":"26","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Li F, Zhao M, Zhang L, Yan S (2017) Robust neighborhood preserving projection by nuclear\/l2, 1-norm regularization for image feature extraction. IEEE Trans Image Process 26(4):1607\u20131622","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"3066_CR6","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.compmedimag.2010.11.013","volume":"35","author":"X Yuan","year":"2011","unstructured":"Yuan X, Yuan X (2011) Fusion of multi-planar images for improved three-dimensional object reconstruction. Comput Med Imaging Gr 35(5):373\u2013382","journal-title":"Comput Med Imaging Gr"},{"key":"3066_CR7","unstructured":"Efrat N, Glasner D, Apartsin A, Nadler B, Levin A (2013) Accurate blur models vs. image priors in single image super-resolution. In: IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, 1\u20138 Dec, pp 2832\u20132839"},{"key":"3066_CR8","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/978-3-319-10593-2_25","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Chih-Yuan Yang","year":"2014","unstructured":"Yang C, Ma C, Yang M (2014) Single-image super-resolution: a benchmark. In: European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6\u201312 Sept, pp 372\u2013386"},{"key":"3066_CR9","unstructured":"Gu S, Sang N, Ma F (2012) Fast image super resolution via local regression. In: International Conference on Pattern Recognition, ICPR 2012, Tsukuba, Japan, 11\u201315 Nov, pp 3128\u20133131"},{"key":"3066_CR10","doi-asserted-by":"crossref","unstructured":"Yang C, Yang M (2013) Fast direct super-resolution by simple functions. In: IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, 1\u20138 Dec 2013, pp 561\u2013568","DOI":"10.1109\/ICCV.2013.75"},{"issue":"1","key":"3066_CR11","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TCI.2016.2629284","volume":"3","author":"Y Romano","year":"2017","unstructured":"Romano Y, Isidoro J, Milanfar P (2017) RAISR: rapid and accurate image super resolution. IEEE Trans Comput Imaging 3(1):110\u2013125","journal-title":"IEEE Trans Comput Imaging"},{"key":"3066_CR12","doi-asserted-by":"crossref","unstructured":"Gu S, Zuo W, Xie Q, Meng D, Feng X, Zhang L (2015) Convolutional sparse coding for image super-resolution. In: IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7\u201313 Dec 2015, pp 1823\u20131831","DOI":"10.1109\/ICCV.2015.212"},{"key":"3066_CR13","doi-asserted-by":"crossref","unstructured":"Chang H, Yeung D, Xiong Y (2004) Super-resolution through neighbor embedding. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA, 27 June\u20132 July, pp 275\u2013282","DOI":"10.1109\/CVPR.2004.1315043"},{"key":"3066_CR14","doi-asserted-by":"crossref","unstructured":"Timofte R, Smet VD, Gool LV (2013) Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, 1\u20138 Dec, pp 1920\u20131927","DOI":"10.1109\/ICCV.2013.241"},{"key":"3066_CR15","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-319-16817-3_8","volume-title":"Computer Vision -- ACCV 2014","author":"Radu Timofte","year":"2015","unstructured":"Timofte R, Smet VD, Gool LV (2014) A+: adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision, ACCV 2014, Singapore, 1\u20135 Nov, pp 111\u2013126"},{"key":"3066_CR16","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken AP, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"3066_CR17","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, ECCV 2016, Amsterdam, The Netherlands, 11\u201314 Oct, pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"3066_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Chao Dong","year":"2014","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6\u201312 Sept, pp 184\u2013199"},{"key":"3066_CR19","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2016.02.046","volume":"194","author":"Y Liang","year":"2016","unstructured":"Liang Y, Wang J, Zhou S, Gong Y, Zheng N (2016) Incorporating image priors with deep convolutional neural networks for image super-resolution. Neurocomputing 194:340\u2013347","journal-title":"Neurocomputing"},{"issue":"1","key":"3066_CR20","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1109\/TIP.2015.2501749","volume":"25","author":"J Xie","year":"2016","unstructured":"Xie J, Feris RS, Sun M (2016) Edge-guided single depth image super resolution. IEEE Trans Image Process 25(1):428\u2013438","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"3066_CR21","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1109\/TIP.2017.2750403","volume":"26","author":"W Yang","year":"2017","unstructured":"Yang W, Feng J, Yang J, Zhao F, Liu J, Guo Z, Yan S (2017) Deep edge guided recurrent residual learning for image super-resolution. IEEE Trans Image Process 26(12):5895\u20135907","journal-title":"IEEE Trans Image Process"},{"key":"3066_CR22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ins.2018.09.018","volume":"473","author":"H Liu","year":"2019","unstructured":"Liu H, Fu Z, Han J, Shao L, Hou S, Chu Y (2019) Single image super-resolution using multi-scale deep encoder\u2013decoder with phase congruency edge map guidance. Inf Sci 473:44\u201358","journal-title":"Inf Sci"},{"key":"3066_CR23","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"3066_CR24","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"3066_CR25","doi-asserted-by":"crossref","unstructured":"Lai W, Huang J, Ahuja N, Yang M (2017) Deep Laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July, pp 5835\u20135843","DOI":"10.1109\/CVPR.2017.618"},{"key":"3066_CR26","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision, ECCV 2016, Amsterdam, The Netherlands, 11\u201314 Oct, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"3066_CR27","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"3066_CR28","unstructured":"Hu Y, Gao X, Li J, Huang Y, Wang H (2018) Single image super-resolution via cascaded multi-scale cross network. CoRR \narXiv:abs\/1802.08808"},{"key":"3066_CR29","doi-asserted-by":"crossref","unstructured":"Li J, Fang F, Mei K, Zhang G (2018) Multi-scale residual network for image super-resolution. In: European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8\u201314 Sept, pp 527\u2013542","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"3066_CR30","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van\u00a0der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"3066_CR31","doi-asserted-by":"crossref","unstructured":"Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22\u201329 Oct, pp 4809\u20134817","DOI":"10.1109\/ICCV.2017.514"},{"key":"3066_CR32","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"3066_CR33","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Loy CC (2018) ESRGAN: enhanced super-resolution generative adversarial networks. In: European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8\u201314 Sept, pp 63\u201379"},{"key":"3066_CR34","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn K (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8\u201314 Sept, pp 256\u2013272","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"3066_CR35","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July, pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"1","key":"3066_CR36","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao H, Gallo O, Frosio I, Kautz J (2017) Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging 3(1):47\u201357","journal-title":"IEEE Trans Comput Imaging"},{"key":"3066_CR37","unstructured":"Bruna J, Sprechmann P, LeCun Y (2016) Super-resolution with deep convolutional sufficient statistics. In: International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May"},{"key":"3066_CR38","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May"},{"issue":"2","key":"3066_CR39","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SGI Armato","year":"2011","unstructured":"Armato SGI, Mclennan G, Bidaut L, Mcnitt-Gray MF, Clarke LP (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915\u2013931","journal-title":"Med Phys"},{"key":"3066_CR40","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May"},{"key":"3066_CR41","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7\u201313 Dec, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"issue":"13","key":"3066_CR42","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1049\/el:20080522","volume":"44","author":"Q Huynh-Thu","year":"2008","unstructured":"Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image\/video quality assessment. Electron Lett 44(13):800\u2013801","journal-title":"Electron Lett"},{"issue":"4","key":"3066_CR43","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"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-019-03066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-019-03066-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-019-03066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:44:59Z","timestamp":1604796299000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-019-03066-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,9]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["3066"],"URL":"https:\/\/doi.org\/10.1007\/s11227-019-03066-3","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2019,11,9]]},"assertion":[{"value":"9 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}