{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:05:51Z","timestamp":1777284351529,"version":"3.51.4"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031442094","type":"print"},{"value":"9783031442100","type":"electronic"}],"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\/978-3-031-44210-0_19","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"232-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["DWA: Differential Wavelet Amplifier for\u00a0Image Super-Resolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0290-7904","authenticated-orcid":false,"given":"Brian B.","family":"Moser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2700-5031","authenticated-orcid":false,"given":"Stanislav","family":"Frolov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8604-6207","authenticated-orcid":false,"given":"Federico","family":"Raue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8656-9569","authenticated-orcid":false,"given":"Sebastian","family":"Palacio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"19_CR1","unstructured":"Agarap, A.F.: Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375 (2018)"},{"key":"19_CR2","unstructured":"Agarwal, A., Lang, J.: Foundations of analog and digital electronic circuits. Elsevier (2005)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: 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 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Canh, T.N., Jeon, B.: Difference of convolution for deep compressive sensing. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2105\u20132109. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803165"},{"issue":"2","key":"19_CR6","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.: 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":"19_CR7","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":"19_CR8","unstructured":"Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems 21 (2008)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Guo, T., Seyed Mousavi, H., Huu Vu, T., Monga, V.: Deep wavelet prediction for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 104\u2013113 (2017)","DOI":"10.1109\/CVPRW.2017.148"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR11","unstructured":"Horowitz, P., Hill, W., Robinson, I.: The art of electronics, vol. 2. Cambridge University Press Cambridge (1989)"},{"key":"19_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Knutsson, H., Westin, C.F.: Normalized and differential convolution. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 515\u2013523. IEEE (1993)","DOI":"10.1109\/CVPR.1993.341081"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Laplante, P.A.: Comprehensive Dictionary of Electrical Engineering. CRC Press (2018)","DOI":"10.1201\/9781420037807"},{"key":"19_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2022.01.029","volume":"479","author":"H Li","year":"2022","unstructured":"Li, H., et al.: SRDIFF: single image super-resolution with diffusion probabilistic models. Neurocomputing 479, 47\u201359 (2022)","journal-title":"Neurocomputing"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SWINIR: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Liu, P., Zhang, H., Zhang, K., Lin, L., Zuo, W.: Multi-level wavelet-cnn for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 773\u2013782 (2018)","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"19_CR18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vision"},{"issue":"2","key":"19_CR19","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004\u20131016 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: 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, ICCV 2001, vol. 2, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Moser, B.B., Raue, F., Frolov, S., Palacio, S., Hees, J., Dengel, A.: Hitchhiker\u2019s guide to super-resolution: Introduction and recent advances. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201321 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3243794","DOI":"10.1109\/TPAMI.2023.3243794"},{"key":"19_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"19_CR23","unstructured":"Sahak, H., Watson, D., Saharia, C., Fleet, D.: Denoising diffusion probabilistic models for robust image super-resolution in the wild. arXiv preprint arXiv:2302.07864 (2023)"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.04.025","volume":"116","author":"M Sar\u0131g\u00fcl","year":"2019","unstructured":"Sar\u0131g\u00fcl, M., Ozyildirim, B.M., Avci, M.: Differential convolutional neural network. Neural Networks 116, 279\u2013287 (2019)","journal-title":"Neural Networks"},{"key":"19_CR26","unstructured":"Stephane, M.: A wavelet tour of signal processing (1999)"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Sun, L., Dong, J., Tang, J., Pan, J.: Spatially-adaptive feature modulation for efficient image super-resolution. arXiv preprint arXiv:2302.13800 (2023)","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"19_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: training real-world blind super-resolution with pure synthetic data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1905\u20131914, October 2021","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"19_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Wang","year":"2019","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63\u201379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"issue":"10","key":"19_CR31","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Chen, J., Hoi, S.C.H.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365\u20133387 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2982166","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-642-27413-8_47","volume-title":"Curves and Surfaces","author":"R Zeyde","year":"2012","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711\u2013730. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44210-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T15:29:09Z","timestamp":1730129349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44210-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442094","9783031442100"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44210-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}