{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:48:26Z","timestamp":1771613306733,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030893620","type":"print"},{"value":"9783030893637","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\/978-3-030-89363-7_35","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"461-472","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3114-9206","authenticated-orcid":false,"given":"Yongsong","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0914-2131","authenticated-orcid":false,"given":"Zetao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1562-8098","authenticated-orcid":false,"given":"Qingzhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoming","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"35_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 [cs, stat], January 2017"},{"key":"35_CR2","volume-title":"Learning OpenCV: Computer Vision with the OpenCV Library","author":"G Bradski","year":"2008","unstructured":"Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O\u2019Reilly Media Inc., Sebastopol (2008)"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"issue":"2","key":"35_CR4","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., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"35_CR5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR6","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 \u2013 ECCV 2016","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":"35_CR7","unstructured":"Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv:1406.2661 [cs, stat], June 2014"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Haris, M., et al.: Deep back-projection networks for super-resolution. In: Proceedings of the IEEE Conference on CVPR, pp. 1664\u20131673 (2018)","DOI":"10.1109\/CVPR.2018.00179"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks. arXiv:1603.05027 [cs], March 2016","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"35_CR10","unstructured":"Huang, Y.: Hetsrwgan-dataset, September 2019. https:\/\/figshare.com\/articles\/dataset\/HetSRWGAN-dataset\/9862184\/2"},{"key":"35_CR11","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/LSP.2021.3077801","volume":"28","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Jiang, Z., Lan, R., Zhang, S., Pi, K.: Infrared image super-resolution via transfer learning and PSRGAN. IEEE Signal Process. Lett. 28, 982\u2013986 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Hui, Z., et al.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th ACM MM, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"35_CR13","unstructured":"Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 [cs], February 2015"},{"key":"35_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs], December 2014"},{"key":"35_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097\u20131105. Curran Associates, Inc. (2012)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on CVPR, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"issue":"03","key":"35_CR17","doi-asserted-by":"publisher","first-page":"1850018","DOI":"10.1142\/S0219691318500182","volume":"16","author":"Y Liu","year":"2017","unstructured":"Liu, Y., Chen, X., Cheng, J., Peng, H., Wang, Z.: Infrared and visible image fusion with convolutional neural networks. Int. J. Wavelets Multiresolut. Inf. Process. 16(03), 1850018 (2017)","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Nah, S., et al.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on CVPR, pp. 3883\u20133891 (2017)","DOI":"10.1109\/CVPR.2017.35"},{"key":"35_CR19","unstructured":"Odena, A.: Faster Asynchronous SGD. arXiv:1601.04033 [cs, stat], January 2016"},{"key":"35_CR20","unstructured":"Radford, A., et al.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on CVPR, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Singh, P., Verma, V.K., Rai, P., Namboodiri, V.P.: HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs. arXiv:1903.04120 [cs], March 2019","DOI":"10.1109\/CVPR.2019.00497"},{"key":"35_CR23","unstructured":"Socarr\u00e1s, Y., Ramos, S., V\u00e1zquez, D., L\u00f3pez, A.M., Gevers, T.: Adapting pedestrian detection from synthetic to far infrared images. In: ICCV Workshops, vol. 3 (2013)"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going Deeper With Convolutions, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Zhang, L.: Deep plug-and-play super-resolution for arbitrary blur kernels. In: IEEE Conference on CVPR, pp. 1671\u20131681 (2019)","DOI":"10.1109\/CVPR.2019.00177"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on CVPR, pp. 3262\u20133271 (2018)","DOI":"10.1109\/CVPR.2018.00344"},{"key":"35_CR28","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.infrared.2017.05.007","volume":"83","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Zhang, L., Bai, X., Zhang, L.: Infrared and visual image fusion through infrared feature extraction and visual information preservation. Infrared Phys. Technol. 83, 227\u2013237 (2017)","journal-title":"Infrared Phys. Technol."}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89363-7_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:21:48Z","timestamp":1635729708000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89363-7_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893620","9783030893637"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89363-7_35","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":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","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":"382","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":"93","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":"28","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":"24% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}