{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:36:22Z","timestamp":1743111382653,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819959679"},{"type":"electronic","value":"9789819959686"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-5968-6_12","type":"book-chapter","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T23:04:42Z","timestamp":1694732682000},"page":"165-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight and Efficient Attention-Based Superresolution Generative Adversarial Networks"],"prefix":"10.1007","author":[{"given":"Shushu","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hefan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Sang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianjiao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"issue":"21","key":"12_CR1","doi-asserted-by":"publisher","first-page":"3554","DOI":"10.3390\/electronics11213554","volume":"11","author":"J Shang","year":"2022","unstructured":"Shang, J., Zhang, X., Zhang, G., et al.: Gated multi-attention feedback network for medical image super-resolution. Electronics 11(21), 3554 (2022)","journal-title":"Electronics"},{"issue":"5","key":"12_CR2","doi-asserted-by":"publisher","first-page":"6652","DOI":"10.3934\/mbe.2021330","volume":"18","author":"H Li","year":"2021","unstructured":"Li, H., Zheng, Q., Yan, W., et al.: Image superresolution reconstruction for secure data transmission in Internet of Things environment. Math. Biosci. Eng. 18(5), 6652\u20136672 (2021)","journal-title":"Math. Biosci. Eng."},{"key":"12_CR3","first-page":"1","volume":"60","author":"S Jia","year":"2022","unstructured":"Jia, S., Wang, Z., Li, Q., et al.: Multiattention generative adversarial network for remote sensing image superresolution. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"He, J., Shi, W., Chen, K., et al.: Gcfsr: a generative and controllable face super resolution method without facial and gan priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1889\u20131898 (2022)","DOI":"10.1109\/CVPR52688.2022.00193"},{"issue":"4","key":"12_CR5","doi-asserted-by":"publisher","first-page":"4142","DOI":"10.1109\/TVT.2022.3151674","volume":"71","author":"Y Ma","year":"2022","unstructured":"Ma, Y., Zeng, Y., Sun, S.: A deep learning based super resolution DOA estimator with single snapshot mimo radar data. IEEE Trans. Vehicular Technol. 71(4), 4142\u20134155 (2022)","journal-title":"IEEE Trans. Vehicular Technol."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Singhal, N., Krahenbuhl, P.: Video compression through image interpolation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 416\u2013431 (2018)","DOI":"10.1007\/978-3-030-01237-3_26"},{"issue":"6","key":"12_CR7","doi-asserted-by":"publisher","first-page":"2942","DOI":"10.1109\/TIP.2018.2814210","volume":"27","author":"H Irmak","year":"2018","unstructured":"Irmak, H., Akar, G.B., Yuksel, S.E.: A map-based approach for hyperspectral imagery superresolution. IEEE Trans. Image Process. 27(6), 2942\u20132951 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"12_CR8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.optlastec.2018.01.043","volume":"110","author":"N Liu","year":"2019","unstructured":"Liu, N., Xu, X., Li, Y., et al.: Sparse representation based image superresolution on the KNN based dictionaries. Opt. Laser Technol. 110, 135\u2013144 (2019)","journal-title":"Opt. Laser Technol."},{"issue":"1","key":"12_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11220-019-0262-y","volume":"21","author":"W Wang","year":"2020","unstructured":"Wang, W., Hu, Y., Luo, Y., et al.: Brief survey of single image superresolution reconstruction based on deep learning approaches. Sens. Imag. 21(1), 1\u201320 (2020)","journal-title":"Sens. Imag."},{"issue":"2","key":"12_CR10","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K.M., et al.: Image superresolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Shi, W.Z., Caballero, J., Husz\u00e1r, F., et al.: Real- time single image and video superresolution using an efficient subpixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Washington, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image superresolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Deeply recursive convolutional network for image superresolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637\u20131645 (2016)","DOI":"10.1109\/CVPR.2016.181"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., et al.: Enhanced deep residual networks for single image superresolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"12_CR15","unstructured":"Bhardwaj, K., Milosavljevic, M., O'Neil, L., et al.: Collapsible linear blocks for superefficient super resolution. arXiv:2103.09404 (2021)"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Zhang, L.: Edge-oriented convolution block for real-time super resolution on mobile devices. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4034\u20134043 (2021)","DOI":"10.1145\/3474085.3475291"},{"issue":"1","key":"12_CR17","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1134\/S1054661822010059","volume":"32","author":"G Pandey","year":"2022","unstructured":"Pandey, G., Ghanekar, U.: A conspectus of deep learning techniques for single-image superresolution. Pattern Recognit. Image Anal. 32(1), 11\u201332 (2022)","journal-title":"Pattern Recognit. Image Anal."},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., et al.: Photo-realistic single image superresolution using a generative adversarial network. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21\u201326 July 2017, pp. 105\u2013114. IEEE Computer Society, Washington (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part V","author":"X Wang","year":"2019","unstructured":"Wang, X., Ke, Y., Shixiang, W., Jinjin, G., Liu, Y., Chao Dong, Y., Qiao, C.C., Loy,: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taix\u00e9, L., Roth, S. (eds.) Computer Vision \u2013 ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part V, pp. 63\u201379. Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Chen, W., Ma, Y., Liu, X., et al.: Hierarchical generative adversarial networks for single image superresolution. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 355\u2013364 (2021)","DOI":"10.1109\/WACV48630.2021.00040"},{"issue":"3","key":"12_CR21","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.31.3.033011","volume":"31","author":"J Lei","year":"2022","unstructured":"Lei, J., Xue, H., Yang, S., et al.: HFF-SRGAN: superresolution generative adversarial network based on high-frequency feature fusion. J. Electron. Imaging 31(3), 033011 (2022)","journal-title":"J. Electron. Imaging"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Jia, M., Lu, M., Sang, Y.: Advanced generative adversarial network for image superresolution. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds.) ICPCSEE 2022. CCIS, vol. 1628, pp. 193\u2013208. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-19-5194-7_15","DOI":"10.1007\/978-981-19-5194-7_15"},{"key":"12_CR23","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Proceedings of the Annual Conference on Neural Information Processing Systems, pp. 2672\u20132680.Curran Associates, Red Hook\/Montreal (2014)"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Tang, H., Xu, D., Yan, Y., et al.: Local class-specific and global image-level generative adversarial networks for semantic-guided scene generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7870\u20137879 (2020)","DOI":"10.1109\/CVPR42600.2020.00789"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, L., Mo, Q., Lin, S., et al.: Uctgan: Diverse image inpainting based on unsupervised cross-space translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5741\u20135750 (2020)","DOI":"10.1109\/CVPR42600.2020.00578"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Nauata, N., Hosseini, S., Chang, K. H., et al.: House-gan++: generative adversarial layout refinement network towards intelligent computational agent for professional architects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13632\u201313641 (2021)","DOI":"10.1109\/CVPR46437.2021.01342"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"12_CR28","unstructured":"Zhang, H., Zu, K., Lu, J.E.: An Efficient Pyramid Split Attention Block on Convolutional Neural Network. arXiv:2105.14447 (2021)"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Abrahamyan, L., Truong, A.M., Philips, W., et al.: Gradient variance loss for structure-enhanced image superresolution. In: ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3219\u20133223 (2022)","DOI":"10.1109\/ICASSP43922.2022.9747387"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., et al.: Ntire 2017 challenge on single image superresolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., et al.: Low-complexity single-image superresolution based on nonnegative neighbor embedding. In: Proceedings of the 23rd British Machine Vision Conference (BMVC), pp. 135.1\u2013135.10. BMVA Press (2012)","DOI":"10.5244\/C.26.135"},{"issue":"03","key":"12_CR32","first-page":"249","volume":"47","author":"YN Jiang","year":"2021","unstructured":"Jiang, Y.N., Li, J.H., Zhao, J.L.: A superresolution reconstruction algorithm for images based on generative adversarial networks. Comput. Eng. 47(03), 249\u2013255 (2021)","journal-title":"Comput. Eng."},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision, pp. 416\u2013423 (2001)","DOI":"10.1109\/ICCV.2001.937655"}],"container-title":["Communications in Computer and Information Science","Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-5968-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T03:20:35Z","timestamp":1730085635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-5968-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819959679","9789819959686"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-5968-6_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPCSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of Pioneering Computer Scientists, Engineers and Educators","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpcsee2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.icpcsee.org\/","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":"244","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":"52","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":"14","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":"21% - 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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}