{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T04:24:38Z","timestamp":1746419078136,"version":"3.37.3"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"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":["Wireless Netw"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11276-020-02496-9","type":"journal-article","created":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T09:22:34Z","timestamp":1610097754000},"page":"3633-3642","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["High fidelity single image blind deblur via GAN"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0533-2691","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenlong","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"issue":"2","key":"2496_CR1","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295\u2013307.","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"2496_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018). Image super-resolution using very deep residual channel attention networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 286\u2013301).","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"2496_CR3","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., & Gao, X. (2018). Fast and accurate single image super-resolution via information distillation network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 723\u2013731).","DOI":"10.1109\/CVPR.2018.00082"},{"key":"2496_CR4","doi-asserted-by":"publisher","unstructured":"Lan, R., Sun, L., Liu, Z., Lu, H., Pang, C., & Luo, X. (2020). Madnet: A fast and lightweight network for single-image super resolution. IEEE Transactions on Cybernetics. https:\/\/doi.org\/10.1109\/TCYB.2020.2970104.","DOI":"10.1109\/TCYB.2020.2970104"},{"issue":"4","key":"2496_CR5","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1109\/TMM.2017.2760100","volume":"20","author":"W Zhao","year":"2017","unstructured":"Zhao, W., Huimin, L., & Wang, D. (2017). Multisensor image fusion and enhancement in spectral total variation domain. IEEE Transactions on Multimedia, 20(4), 866\u2013879.","journal-title":"IEEE Transactions on Multimedia"},{"key":"2496_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., & Zhang, L. (2018). Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3262\u20133271).","DOI":"10.1109\/CVPR.2018.00344"},{"key":"2496_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., & Zhang, L. (2019). Deep plug-and-play super-resolution for arbitrary blur kernels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1671\u201316810.","DOI":"10.1109\/CVPR.2019.00177"},{"key":"2496_CR8","doi-asserted-by":"crossref","unstructured":"Shocher, A, Cohen, N., & Irani, M. l. (2018). \u201czero-shot\u201d super-resolution using deep internal learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3118\u20133126).","DOI":"10.1109\/CVPR.2018.00329"},{"key":"2496_CR9","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, H., Zuo, W., & Dong, C. (2019). Blind super-resolution with iterative kernel correction. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1604\u20131613).","DOI":"10.1109\/CVPR.2019.00170"},{"issue":"6","key":"2496_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356575","volume":"38","author":"V Cornillere","year":"2019","unstructured":"Cornillere, V., Djelouah, A., Yifan, W., Sorkine-Hornung, O., & Schroers, C. (2019). Blind image super-resolution with spatially variant degradations. ACM Transactions on Graphics (TOG), 38(6), 1\u201313.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"2496_CR11","doi-asserted-by":"crossref","unstructured":"Mahendran, A., & Vedaldi, A. (2015). Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp 5188\u20135196).","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"2496_CR12","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 (pp. 694\u2013711). Springer.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"2496_CR13","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., & Wang, Z. et\u00a0al. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp 4681\u20134690).","DOI":"10.1109\/CVPR.2017.19"},{"key":"2496_CR14","doi-asserted-by":"crossref","unstructured":"Sajjadi, M. S. M., Scholkopf, B., & Hirsch, M. (2017). Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE International Conference on Computer Vision (pp 4491\u20134500).","DOI":"10.1109\/ICCV.2017.481"},{"key":"2496_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. A., Shechtman, E., Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 586\u2013595).","DOI":"10.1109\/CVPR.2018.00068"},{"key":"2496_CR16","doi-asserted-by":"crossref","unstructured":"Blau, Y., & Michaeli, T. (2018). The perception-distortion tradeoff. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6228\u20136237).","DOI":"10.1109\/CVPR.2018.00652"},{"key":"2496_CR17","doi-asserted-by":"crossref","unstructured":"Xu, J., Chae, Y., Stenger, B., & Datta, A. (2018). Dense bynet: Residual dense network for image super resolution. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 71\u201375). IEEE.","DOI":"10.1109\/ICIP.2018.8451696"},{"key":"2496_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C. C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 0-0).","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"2496_CR19","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R. (2017). Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 126\u2013135).","DOI":"10.1109\/CVPRW.2017.150"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-020-02496-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-020-02496-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-020-02496-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T15:42:19Z","timestamp":1720021339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-020-02496-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":19,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2496"],"URL":"https:\/\/doi.org\/10.1007\/s11276-020-02496-9","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"type":"print","value":"1022-0038"},{"type":"electronic","value":"1572-8196"}],"subject":[],"published":{"date-parts":[[2021,1,8]]},"assertion":[{"value":"3 November 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}