{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:05:41Z","timestamp":1742911541588,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873608"},{"type":"electronic","value":"9783030873615"}],"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\/978-3-030-87361-5_25","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"303-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Boosting Channel Attention for\u00a0Real Image Denoising: Sub-band Pyramid Attention"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9143-4741","authenticated-orcid":false,"given":"Huayu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0070-9058","authenticated-orcid":false,"given":"Haiyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8006-4383","authenticated-orcid":false,"given":"Xiwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3035-3064","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3330-6132","authenticated-orcid":false,"given":"Abolfazl","family":"Razi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692\u20131700 (2018)","DOI":"10.1109\/CVPR.2018.00182"},{"key":"25_CR2","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":"11","key":"25_CR3","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"54","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Sig. Process."},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3155\u20133164 (2019)","DOI":"10.1109\/ICCV.2019.00325"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Anwar, S., Barnes, N.: Densely residual Laplacian super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/TPAMI.2020.3021088"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Anwar, S., Phuoc Huynh, C., Porikli, F.: Identity enhanced residual image denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 520\u2013521 (2020)","DOI":"10.1109\/CVPRW50498.2020.00268"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Bao, L., Yang, Z., Wang, S., Bai, D., Lee, J.: Real image denoising based on multi-scale residual dense block and cascaded U-Net with block-connection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 448\u2013449 (2020)","DOI":"10.1109\/CVPRW50498.2020.00232"},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1109\/34.141557","volume":"6","author":"RA Boie","year":"1992","unstructured":"Boie, R.A., Cox, I.J.: An analysis of camera noise. IEEE Trans. Pattern Anal. Mach. Intell. 6, 671\u2013674 (1992)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Boyat, A.K., Joshi, B.K.: A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489 (2015)","DOI":"10.5121\/sipij.2015.6206"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60\u201365. IEEE (2005)","DOI":"10.1109\/CVPR.2005.38"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392\u20132399. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247952"},{"issue":"6","key":"25_CR12","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."},{"issue":"8","key":"25_CR13","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"issue":"10","key":"25_CR15","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","volume":"17","author":"A Foi","year":"2008","unstructured":"Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737\u20131754 (2008)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862\u20132869 (2014)","DOI":"10.1109\/CVPR.2014.366"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1712\u20131722 (2019)","DOI":"10.1109\/CVPR.2019.00181"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"25_CR19","unstructured":"Jain, U.: Characterization of CMOS Image Sensor. Ph.D. thesis, MS Thesis, Delft University of Technology (2016)"},{"key":"25_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"10","key":"25_CR21","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1109\/TIP.2014.2347204","volume":"23","author":"X Liu","year":"2014","unstructured":"Liu, X., Tanaka, M., Okutomi, M.: Practical signal-dependent noise parameter estimation from a single noisy image. IEEE Trans. Image Process. 23(10), 4361\u20134371 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR22","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, pp. 2802\u20132810 (2016)"},{"key":"25_CR23","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 8th IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"key":"25_CR24","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)"},{"key":"25_CR25","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1586\u20131595 (2017)","DOI":"10.1109\/CVPR.2017.294"},{"issue":"2","key":"25_CR27","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s11263-008-0197-6","volume":"82","author":"S Roth","year":"2009","unstructured":"Roth, S., Black, M.J.: Fields of experts. Int. J. Comput. Vis. 82(2), 205 (2009)","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"25_CR28","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":"25_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-030-01237-3_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Xu","year":"2018","unstructured":"Xu, J., Zhang, L., Zhang, D.: A trilateral weighted sparse coding scheme for real-world image denoising. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 21\u201338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_2"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, L., Zhang, D., Feng, X.: Multi-channel weighted nuclear norm minimization for real color image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1096\u20131104 (2017)","DOI":"10.1109\/ICCV.2017.125"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., et al.: CycleISP: real image restoration via improved data synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2696\u20132705 (2020)","DOI":"10.1109\/CVPR42600.2020.00277"},{"issue":"7","key":"25_CR32","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":"25_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929\u20133938 (2017)","DOI":"10.1109\/CVPR.2017.300"},{"issue":"9","key":"25_CR34","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Jiang, Z., Men, A., Ju, G.: Pyramid real image denoising network. In: 2019 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/VCIP47243.2019.8965754"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87361-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T01:26:59Z","timestamp":1725845219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87361-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873608","9783030873615"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87361-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.cn\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"421","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":"198","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":"47% - 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":"3","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)"}},{"value":"Conference was postponed due to the COVID19 pandemic.","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)"}}]}}