{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T06:53:22Z","timestamp":1773212002485,"version":"3.50.1"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729720","type":"print"},{"value":"9783031729737","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72973-7_21","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"359-375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for\u00a0Efficient Image Super-Resolution"],"prefix":"10.1007","author":[{"given":"Mingjun","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Long","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jiangxin","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Jinshan","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., Sohn, K.A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: ECCV (2018)","DOI":"10.1109\/CVPRW.2018.00123"},{"issue":"5","key":"21_CR2","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbel\u00e1ez","year":"2011","unstructured":"Arbel\u00e1ez, P., Maire, M., Fowlkes, C.C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33(5), 898\u2013916 (2011)","journal-title":"PAMI"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi\u00a0Morel, M.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC (2012)","DOI":"10.5244\/C.26.135"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Dong, C.: Activating more pixels in image super-resolution transformer. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Choi, H., Lee, J., Yang, J.: N-gram in swin transformers for efficient lightweight image super-resolution. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00206"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. In: CVPR (2007)","DOI":"10.1109\/CVPR.2007.383028"},{"issue":"2","key":"21_CR9","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. PAMI 38(2), 295\u2013307 (2016)","journal-title":"PAMI"},{"key":"21_CR10","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":"21_CR11","doi-asserted-by":"crossref","unstructured":"Dong, J., Pan, J., Yang, Z., Tang, J.: Multi-scale residual low-pass filter network for image deblurring. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01134"},{"key":"21_CR12","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00908"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., Xia, S.T.: Mambair: a simple baseline for image restoration with state-space model. arXiv preprint arXiv:2402.15648 (2024)","DOI":"10.1007\/978-3-031-72649-1_13"},{"key":"21_CR15","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units. arXiv preprint arXiv:1606.08415 (2016)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: ACM MM (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"21_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Li, A., Zhang, L., Liu, Y., Zhu, C.: Feature modulation transformer: cross-refinement of global representation via high-frequency prior for image super-resolution. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01150"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Li, M., Ma, B., Zhang, Y.: Lightweight image super-resolution with pyramid clustering transformer. In: TCSVT, p. 1 (2023)","DOI":"10.1109\/TCSVT.2023.3296526"},{"key":"21_CR22","unstructured":"Li, W., Zhou, K., Qi, L., Jiang, N., Lu, J., Jia, J.: LAPAR: linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. In: NeurIPS (2020)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Efficient and explicit modelling of image hierarchies for image restoration. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01753"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Blueprint separable residual network for efficient image super-resolution. In: CVPR Workshops (2022)","DOI":"10.1109\/CVPRW56347.2022.00099"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: ICCV Workshops (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: CVPR Workshops (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Liu, J., Chen, C., Tang, J., Wu, G.: From coarse to fine: hierarchical pixel integration for lightweight image super-resolution. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i2.25254"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: ECCV Workshops (2020)","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"21_CR30","unstructured":"Loshchilov, I., Hutter, F.: SQDR: stochastic gradient descent with warm restarts. In: ICLR (2017)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., Zeng, T.: Transformer for single image super-resolution. In: CVPR Workshops (2022)","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Mao, Y., et al.: Multi-level dispersion residual network for efficient image super-resolution. In: CVPR Workshops (2023)","DOI":"10.1109\/CVPRW59228.2023.00167"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Matsui, Y., Ito, K., Aramaki, Y., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. arXiv preprint arXiv:1510.04389 (2015)","DOI":"10.1007\/s11042-016-4020-z"},{"key":"21_CR34","unstructured":"Park, N., Kim, S.: How do vision transformers work? In: ICLR (2022)"},{"key":"21_CR35","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: CVPR (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Sun, L., Dong, J., Tang, J., Pan, J.: Spatially-adaptive feature modulation for efficient image super-resolution. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"21_CR37","unstructured":"Sun, L., Pan, J., Tang, J.: ShuffleMixer: an efficient convnet for image super-resolution. In: NeurIPS (2022)"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van\u00a0Gool, L., Yang, M.H., Zhang, L., et\u00a0al.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: CVPR Workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Timofte, R., DeSmet, V., Van\u00a0Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: ACCV 2014 (2015)","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"21_CR40","unstructured":"Vaswani, S., Parmar, U., Jones, G., Kaiser, U.L., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, X., Ni, B., Liu, Y., Liu, J.: Omni aggregation networks for lightweight image super-resolution. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.02143"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Exploring sparsity in image super-resolution for efficient inference. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00488"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: PVTV 2: improved baselines with pyramid vision transformer. Comput. Visual Media 8(3), 1\u201310 (2022)","DOI":"10.1007\/s41095-022-0274-8"},{"issue":"11","key":"21_CR44","first-page":"2861","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. TIP 19(11), 2861\u20132873 (2010)","journal-title":"Image super-resolution via sparse representation. TIP"},{"key":"21_CR45","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"21_CR46","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces (2012)","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"21_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, A., Ren, W., Liu, Y., Cao, X.: Lightweight image super-resolution with superpixel token interaction. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01169"},{"key":"21_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Minivit: compressing vision transformers with weight multiplexing. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01183"},{"key":"21_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"21_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"21_CR52","doi-asserted-by":"publisher","unstructured":"Zhou, L., et al.: Efficient image super-resolution using vast-receptive-field attention. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022, Part II, pp. 256\u2013272. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25063-7_16","DOI":"10.1007\/978-3-031-25063-7_16"},{"key":"21_CR53","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Li, Z., Guo, C., Bai, S., Cheng, M., Hou, Q.: Srformer: permuted self-attention for single image super-resolution. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01174"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72973-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T15:00:01Z","timestamp":1739631601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72973-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031729720","9783031729737"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72973-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","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":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}