{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:47:46Z","timestamp":1778255266145,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981779","type":"print"},{"value":"9789819981786","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8178-6_42","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T10:02:54Z","timestamp":1701252174000},"page":"556-568","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dual-Domain Learning for\u00a0JPEG Artifacts Removal"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4899-3248","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0589-6694","authenticated-orcid":false,"given":"Lu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1740-0804","authenticated-orcid":false,"given":"Chen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8408-0527","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"42_CR1","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"},{"issue":"1","key":"42_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"issue":"5","key":"42_CR3","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898\u2013916 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Cavigelli, L., Hager, P., Benini, L.: CAS-CNN: a deep convolutional neural network for image compression artifact suppression. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 752\u2013759. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7965927"},{"issue":"6","key":"42_CR5","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2016","unstructured":"Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256\u20131272 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR6","unstructured":"Chi, L., Jiang, B., Mu, Y.: Fast Fourier convolution. In: Advances in Neural Information Processing Systems, vol. 33, pp. 4479\u20134488 (2020)"},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Dong, C., Deng, Y., Loy, C.C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576\u2013584 (2015)","DOI":"10.1109\/ICCV.2015.73"},{"key":"42_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-58598-3_18","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Ehrlich","year":"2020","unstructured":"Ehrlich, M., Davis, L., Lim, S.-N., Shrivastava, A.: Quantization guided JPEG artifact correction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 293\u2013309. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_18"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Ehrlich, M., Davis, L.S.: Deep residual learning in the JPEG transform domain. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3484\u20133493 (2019)","DOI":"10.1109\/ICCV.2019.00358"},{"issue":"5","key":"42_CR10","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TIP.2007.891788","volume":"16","author":"A Foi","year":"2007","unstructured":"Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Trans. Image Process. 16(5), 1395\u20131411 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Fu, X., Zha, Z.J., Wu, F., Ding, X., Paisley, J.: JPEG artifacts reduction via deep convolutional sparse coding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2501\u20132510 (2019)","DOI":"10.1109\/ICCV.2019.00259"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Galteri, L., Seidenari, L., Bertini, M., Del Bimbo, A.: Deep generative adversarial compression artifact removal. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4826\u20134835 (2017)","DOI":"10.1109\/ICCV.2017.517"},{"issue":"8","key":"42_CR13","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TMM.2019.2895280","volume":"21","author":"L Galteri","year":"2019","unstructured":"Galteri, L., Seidenari, L., Bertini, M., Del Bimbo, A.: Deep universal generative adversarial compression artifact removal. IEEE Trans. Multimed. 21(8), 2131\u20132145 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"42_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46448-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Guo","year":"2016","unstructured":"Guo, J., Chao, H.: Building Dual-domain representations for compression artifacts reduction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_38"},{"key":"42_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-031-19800-7_10","volume-title":"Computer Vision \u2013 ECCV 2022","author":"J Huang","year":"2022","unstructured":"Huang, J., et al.: Deep Fourier-based exposure correction network with spatial-frequency interaction. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13679, pp. 163\u2013180. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19800-7_10"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhang, K., Timofte, R.: Towards flexible blind jpeg artifacts removal. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4997\u20135006 (2021)","DOI":"10.1109\/ICCV48922.2021.00495"},{"key":"42_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"42_CR18","unstructured":"Li, Z., et al.: Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895 (2020)"},{"key":"42_CR19","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":"42_CR20","doi-asserted-by":"crossref","unstructured":"Liu, T., Cheng, J., Tan, S.: Spectral Bayesian uncertainty for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18166\u201318175 (2023)","DOI":"10.1109\/CVPR52729.2023.01742"},{"key":"42_CR21","unstructured":"Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"42_CR22","unstructured":"Mao, X., Liu, Y., Shen, W., Li, Q., Wang, Y.: Deep residual Fourier transformation for single image deblurring. arXiv preprint arXiv:2111.11745 (2021)"},{"key":"42_CR23","doi-asserted-by":"crossref","unstructured":"Ren, J., Liu, J., Li, M., Bai, W., Guo, Z.: Image blocking artifacts reduction via patch clustering and low-rank minimization. In: 2013 Data Compression Conference, pp. 516\u2013516. IEEE (2013)","DOI":"10.1109\/DCC.2013.95"},{"key":"42_CR24","unstructured":"Sheikh, H.: Live image quality assessment database release 2 (2005). http:\/\/live.ece.utexas.edu\/research\/quality"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: 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":"42_CR26","doi-asserted-by":"crossref","unstructured":"Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electr. 38(1), xviii-xxxiv (1992)","DOI":"10.1109\/30.125072"},{"key":"42_CR27","unstructured":"Wang, H., Fan, Y., Wang, Z., Jiao, L., Schiele, B.: Parameter-free spatial attention network for person re-identification. arXiv preprint arXiv:1811.12150 (2018)"},{"key":"42_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-031-19790-1_37","volume-title":"Computer Vision - ECCV 2022","author":"X Wang","year":"2022","unstructured":"Wang, X., Fu, X., Zhu, Y., Zha, Z.J.: JPEG artifacts removal via contrastive representation learning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13677, pp. 615\u2013631. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19790-1_37"},{"key":"42_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., Huang, T.S.: D3: deep dual-domain based fast restoration of JPEG-compressed images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2764\u20132772 (2016)","DOI":"10.1109\/CVPR.2016.302"},{"issue":"4","key":"42_CR30","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"42_CR31","unstructured":"Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"42_CR32","doi-asserted-by":"crossref","unstructured":"Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q.: A Fourier-based framework for domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14383\u201314392 (2021)","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: Multi-stage progressive image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14821\u201314831 (2021)","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"42_CR34","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"},{"issue":"7","key":"42_CR35","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."},{"key":"42_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, W., Hu, Y., Liu, J.: DMCNN: dual-domain multi-scale convolutional neural network for compression artifacts removal. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 390\u2013394. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451694"},{"key":"42_CR37","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)"},{"issue":"7","key":"42_CR38","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1109\/TPAMI.2020.2968521","volume":"43","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480\u20132495 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"42_CR39","doi-asserted-by":"publisher","first-page":"63283","DOI":"10.1109\/ACCESS.2020.2984387","volume":"8","author":"S Zini","year":"2020","unstructured":"Zini, S., Bianco, S., Schettini, R.: Deep residual autoencoder for blind universal JPEG restoration. IEEE Access 8, 63283\u201363294 (2020)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8178-6_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T07:38:48Z","timestamp":1730705928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8178-6_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9789819981779","9789819981786"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8178-6_42","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"30 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"0","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}