{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:51:18Z","timestamp":1770817878273,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030890285","type":"print"},{"value":"9783030890292","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89029-2_19","type":"book-chapter","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T05:09:24Z","timestamp":1633928964000},"page":"242-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Stream Fusion Network for Multi-Distortion Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Yang","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yupeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinman","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangui","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"issue":"8","key":"19_CR1","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1109\/TVCG.2019.2894627","volume":"26","author":"B Zhang","year":"2020","unstructured":"Zhang, B., Sheng, B., Li, P., Lee, T.: Depth of field rendering using multilayer-neighborhood optimization. IEEE Trans. Vis. Comput. Graph. 26(8), 2546\u20132559 (2020)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"19_CR2","doi-asserted-by":"crossref","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir, A., et al.: OFF-eNET: an optimally fused fully end-to-end network for automatic dense volumetric 3d intracranial blood vessels segmentation. IEEE Trans. Image Process. 29, 7192\u20137202 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184\u2013199 (2014)","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"19_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Lai, W., Huang, J., Ahuja, N., Yang, M.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2017, pp. 5835\u20135843 (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, S., Wang, X.: Learning continuous image representation with local implicit image function, CoRR, vol. abs\/2012.09161 (2020)","DOI":"10.1109\/CVPR46437.2021.00852"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps, CoRR, vol. abs\/2011.11036 (2020)","DOI":"10.1109\/CVPR46437.2021.00908"},{"key":"19_CR10","unstructured":"Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, Curran Associates Inc, pp. 1790\u20131798 (2014). http:\/\/papers.nips.cc\/paper\/5485-deep-convolutional-neural-network-for-image-deconvolution.pdf"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Hradi$$\\check{s}$$, M.: Convolutional neural networks for direct text deblurring. In: British Machine Vision Conference 2015 (2015)","DOI":"10.5244\/C.29.6"},{"key":"19_CR12","unstructured":"Matas, J.: Deblurgan: blind motion deblurring using conditional adversarial networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)"},{"key":"19_CR13","unstructured":"Xin, T., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Madam, N.T., Kumar, S., Rajagopalan, A.N.: Unsupervised class-specific deblurring. In: 15th European Conference, Munich, Germany, 8\u201314 September 2018, proceedings, part x (2018)","DOI":"10.1007\/978-3-030-01249-6_22"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space. In: 2007 IEEE International Conference on Image Processing. IEEE (September 2007)","DOI":"10.1109\/ICIP.2007.4378954"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising, pp. 2862\u20132869 (2014)","DOI":"10.1109\/CVPR.2014.366"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Lefkimmiatis, S.: Non-local color image denoising with convolutional neural networks, pp. 5882\u20135891 (2017)","DOI":"10.1109\/CVPR.2017.623"},{"issue":"7","key":"19_CR18","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., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"19_CR19","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Ffdnet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration, pp. 4549\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.486"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs, pp. 1712\u20131722 (2019)","DOI":"10.1109\/CVPR.2019.00181"},{"key":"19_CR22","unstructured":"Zhang, X., Dong, H., Hu, Z., Lai, W.-S., Wang, F., Yang, M.-H.: Gated fusion network for joint image deblurring and super-resolution, CoRR, vol. abs\/1807.10806 (2018)"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Liang, Z., Zhang, D., Shao, J.: Jointly solving deblurring and super-resolution problems with dual supervised network. In: IEEE International Conference on Multimedia and Expo (ICME), vol. 2019, pp. 790\u2013795 (2019)","DOI":"10.1109\/ICME.2019.00141"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: IEEE International Conference on Computer Vision, pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring, pp. 257\u2013265 (2017)","DOI":"10.1109\/CVPR.2017.35"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89029-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T18:05:10Z","timestamp":1673460310000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89029-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030890285","9783030890292"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89029-2_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cgs-network.org\/cgi21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"131","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}