{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:52Z","timestamp":1740107872421,"version":"3.37.3"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["No.2023YFB3308403","No.2023YFB3308403","No.2023YFB3308403","No.2023YFB3308403"],"award-info":[{"award-number":["No.2023YFB3308403","No.2023YFB3308403","No.2023YFB3308403","No.2023YFB3308403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["No.ZR2022LZH008","No.ZR2022LZH008","No.ZR2022LZH008","No.ZR2022LZH008"],"award-info":[{"award-number":["No.ZR2022LZH008","No.ZR2022LZH008","No.ZR2022LZH008","No.ZR2022LZH008"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The 20 Planned Projects in Jinan","award":["No.2021GXRC046","No.2021GXRC046","No.2021GXRC046","No.2021GXRC046"],"award-info":[{"award-number":["No.2021GXRC046","No.2021GXRC046","No.2021GXRC046","No.2021GXRC046"]}]},{"name":"Basic Research enhancement Program of Qilu University of Technology","award":["No.2021JC02015","No.2021JC02015","No.2021JC02015","No.2021JC02015"],"award-info":[{"award-number":["No.2021JC02015","No.2021JC02015","No.2021JC02015","No.2021JC02015"]}]},{"name":"Basic research projects of Qilu University of Technology","award":["No.2023PY039","No.2023PY039","No.2023PY039","No.2023PY039"],"award-info":[{"award-number":["No.2023PY039","No.2023PY039","No.2023PY039","No.2023PY039"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00530-024-01501-x","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T06:03:00Z","timestamp":1727157780000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight super-resolution via multi-group window self-attention and residual blueprint separable convolution"],"prefix":"10.1007","volume":"30","author":[{"given":"Chen","family":"Liang","sequence":"first","affiliation":[]},{"given":"Hu","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Yuchen","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shengrong","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"1501_CR1","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Task-driven super resolution: Object detection in low-resolution images. In: Proceedings of the International Conference on Neural Information Processing, pp. 387\u2013395 (2021)","DOI":"10.1007\/978-3-030-92307-5_45"},{"issue":"6","key":"1501_CR2","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1109\/TMI.2022.3142610","volume":"41","author":"Y Sui","year":"2022","unstructured":"Sui, Y., Afacan, O., Jaimes, C., Gholipour, A., Warfield, S.K.: Scan-specific generative neural network for mri super-resolution reconstruction. IEEE Trans. Med. Imaging 41(6), 1383\u20131399 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1501_CR3","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Proceedings of the European Conference on Computer Vision, pp. 184\u2013199 (2014)","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"1501_CR4","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu\u00a0Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1501_CR5","doi-asserted-by":"crossref","unstructured":"Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., Shen, H.: Single image super-resolution via a holistic attention network. In: Proceedings of the European Conference on Computer Vision, pp. 191\u2013207 (2020)","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"1501_CR6","first-page":"1","volume":"2","author":"X Yang","year":"2022","unstructured":"Yang, X., Zhu, Y., Guo, Y., Zhou, D.: An image super-resolution network based on multi-scale convolution fusion. Vis. Comput. 2, 1\u201311 (2022)","journal-title":"Vis. Comput."},{"key":"1501_CR7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"1501_CR8","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., Gao, W.: Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299\u201312310 (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"1501_CR9","first-page":"25478","volume":"35","author":"Z Chen","year":"2022","unstructured":"Chen, Z., Zhang, Y., Gu, J., Kong, L., Yuan, X., et al.: Cross aggregation transformer for image restoration. Adv. Neural. Inf. Process. Syst. 35, 25478\u201325490 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1501_CR10","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., Dong, C.: Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"issue":"2","key":"1501_CR11","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s00530-024-01276-1","volume":"30","author":"B Liu","year":"2024","unstructured":"Liu, B., Sun, J., Zhu, B., Li, T., Sun, F.: Madformer: multi-attention-driven image super-resolution method based on transformer. Multimedia Syst. 30(2), 78 (2024)","journal-title":"Multimedia Syst."},{"key":"1501_CR12","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision, pp. 252\u2013268 (2018)","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"1501_CR13","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the ACM International Conference on Multimedia, pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"1501_CR14","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: Proceedings of the European Conference on Computer Vision, pp. 41\u201355 (2020). Springer","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"1501_CR15","doi-asserted-by":"crossref","unstructured":"Kong, F., Li, M., Liu, S., Liu, D., He, J., Bai, Y., Chen, F., Fu, L.: Residual local feature network for efficient super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 766\u2013776 (2022)","DOI":"10.1109\/CVPRW56347.2022.00092"},{"key":"1501_CR16","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, Y., Chen, X., Cai, H., Gu, J., Qiao, Y., Dong, C.: Blueprint separable residual network for efficient image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 833\u2013843 (2022)","DOI":"10.1109\/CVPRW56347.2022.00099"},{"issue":"1","key":"1501_CR17","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00530-022-00976-w","volume":"29","author":"X Gao","year":"2023","unstructured":"Gao, X., Xu, L., Wang, F., Hu, X.: Multi-branch aware module with channel shuffle pixel-wise attention for lightweight image super-resolution. Multimed. Syst. 29(1), 289\u2013303 (2023)","journal-title":"Multimed. Syst."},{"key":"1501_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1501_CR19","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: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"1501_CR20","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., Zeng, T.: Transformer for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 457\u2013466 (2022)","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"1501_CR21","doi-asserted-by":"crossref","unstructured":"Fang, J., Lin, H., Chen, X., Zeng, K.: A hybrid network of cnn and transformer for lightweight image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1103\u20131112 (2022)","DOI":"10.1109\/CVPRW56347.2022.00119"},{"key":"1501_CR22","doi-asserted-by":"crossref","unstructured":"Choi, H., Lee, J., Yang, J.: N-gram in swin transformers for efficient lightweight image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2071\u20132081 (2023)","DOI":"10.1109\/CVPR52729.2023.00206"},{"key":"1501_CR23","unstructured":"Majumder, P., Mitra, M., Chaudhuri, B.: N-gram: a language independent approach to ir and nlp. In: International Conference on Universal Knowledge and Language, vol. 2 (2002)"},{"key":"1501_CR24","doi-asserted-by":"crossref","unstructured":"Haase, D., Amthor, M.: Rethinking depthwise separable convolutions: How intra-kernel correlations lead to improved mobilenets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14600\u201314609 (2020)","DOI":"10.1109\/CVPR42600.2020.01461"},{"key":"1501_CR25","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., Jin, P., Zhao, S., Golmant, N., Gholaminejad, A., Gonzalez, J., Keutzer, K.: Shift: A zero flop, zero parameter alternative to spatial convolutions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9127\u20139135 (2018)","DOI":"10.1109\/CVPR.2018.00951"},{"key":"1501_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1501_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"1501_CR28","doi-asserted-by":"crossref","unstructured":"Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4799\u20134807 (2017)","DOI":"10.1109\/ICCV.2017.514"},{"key":"1501_CR29","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: Proceedings of the European Conference on Computer Vision, pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"1501_CR30","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"1501_CR31","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., Shi, H.: Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5690\u20135699 (2020)","DOI":"10.1109\/CVPR42600.2020.00573"},{"key":"1501_CR32","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3517\u20133526 (2021)","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"1501_CR33","first-page":"58","volume":"2","author":"J-N Su","year":"2022","unstructured":"Su, J.-N., Gan, M., Chen, G.-Y., Yin, J.-L., Chen, C.P.: Global learnable attention for single image super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. 2, 58 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1501_CR34","unstructured":"Li, W., Lu, X., Qian, S., Lu, J., Zhang, X., Jia, J.: On efficient transformer-based image pre-training for low-level vision. arXiv preprint arXiv:2112.10175 (2021)"},{"key":"1501_CR35","unstructured":"Zhang, J., Zhang, Y., Gu, J., Zhang, Y., Kong, L., Yuan, X.: Accurate image restoration with attention retractable transformer. arXiv preprint arXiv:2210.01427 (2022)"},{"key":"1501_CR36","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 723\u2013731 (2018)","DOI":"10.1109\/CVPR.2018.00082"},{"key":"1501_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, H., Kong, X., He, J., Qiao, Y., Dong, C.: Efficient image super-resolution using pixel attention. In: Proceedings of the European Conference on Computer Vision, pp. 56\u201372 (2020)","DOI":"10.1007\/978-3-030-67070-2_3"},{"issue":"3","key":"1501_CR38","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1109\/TCYB.2020.2970104","volume":"51","author":"R Lan","year":"2020","unstructured":"Lan, R., Sun, L., Liu, Z., Lu, H., Pang, C., Luo, X.: Madnet: a fast and lightweight network for single-image super resolution. IEEE Trans. Cybern. 51(3), 1443\u20131453 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"1501_CR39","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.neucom.2022.08.053","volume":"509","author":"G Gendy","year":"2022","unstructured":"Gendy, G., Sabor, N., Hou, J., He, G.: Balanced spatial feature distillation and pyramid attention network for lightweight image super-resolution. Neurocomputing 509, 157\u2013166 (2022)","journal-title":"Neurocomputing"},{"issue":"4","key":"1501_CR40","first-page":"4826","volume":"45","author":"X Luo","year":"2022","unstructured":"Luo, X., Qu, Y., Xie, Y., Zhang, Y., Li, C., Fu, Y.: Lattice network for lightweight image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4826\u20134842 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1501_CR41","doi-asserted-by":"crossref","unstructured":"Xie, C., Zhang, X., Li, L., Meng, H., Zhang, T., Li, T., Zhao, X.: Large kernel distillation network for efficient single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1283\u20131292 (2023)","DOI":"10.1109\/CVPRW59228.2023.00135"},{"key":"1501_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108997","volume":"133","author":"P Behjati","year":"2023","unstructured":"Behjati, P., Rodriguez, P., Fern\u00e1ndez, C., Hupont, I., Mehri, A., Gonz\u00e0lez, J.: Single image super-resolution based on directional variance attention network. Pattern Recogn. 133, 108997 (2023)","journal-title":"Pattern Recogn."},{"issue":"11","key":"1501_CR43","doi-asserted-by":"publisher","first-page":"12949","DOI":"10.1007\/s10462-023-10464-w","volume":"56","author":"C Yuan","year":"2023","unstructured":"Yuan, C., Agaian, S.S.: A comprehensive review of binary neural network. Artif. Intell. Rev. 56(11), 12949\u201313013 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"1501_CR44","doi-asserted-by":"crossref","unstructured":"Agarwal, P., Mathew, M., Patel, K.R., Tripathi, V., Swami, P.: Prune efficiently by soft pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2210\u20132217 (2024)","DOI":"10.1109\/CVPRW63382.2024.00226"},{"key":"1501_CR45","doi-asserted-by":"crossref","unstructured":"Xin, J., Wang, N., Jiang, X., Li, J., Huang, H., Gao, X.: Binarized neural network for single image super resolution. In: Proceedings of the European Conference on Computer Vision, pp. 91\u2013107 (2020). Springer","DOI":"10.1007\/978-3-030-58548-8_6"},{"key":"1501_CR46","doi-asserted-by":"publisher","first-page":"8368","DOI":"10.1109\/TIP.2020.3014953","volume":"29","author":"B Li","year":"2020","unstructured":"Li, B., Wang, B., Liu, J., Qi, Z., Shi, Y.: s-lwsr: Super lightweight super-resolution network. IEEE Trans. Image Process. 29, 8368\u20138380 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1501_CR47","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neunet.2021.08.002","volume":"144","author":"X Jiang","year":"2021","unstructured":"Jiang, X., Wang, N., Xin, J., Xia, X., Yang, X., Gao, X.: Learning lightweight super-resolution networks with weight pruning. Neural Netw. 144, 21\u201332 (2021)","journal-title":"Neural Netw."},{"key":"1501_CR48","doi-asserted-by":"crossref","unstructured":"Jiang, X., Wang, N., Xin, J., Li, K., Yang, X., Gao, X.: Training binary neural network without batch normalization for image super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1700\u20131707 (2021)","DOI":"10.1609\/aaai.v35i2.16263"},{"issue":"3","key":"1501_CR49","doi-asserted-by":"publisher","first-page":"3989","DOI":"10.1109\/TNNLS.2022.3201528","volume":"35","author":"X Jiang","year":"2022","unstructured":"Jiang, X., Wang, N., Xin, J., Li, K., Yang, X., Li, J., Gao, X.: Toward pixel-level precision for binary super-resolution with mixed binary representation. IEEE Trans. Neural Netw. Learn. Syst. 35(3), 3989\u20134001 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1501_CR50","doi-asserted-by":"publisher","first-page":"6234","DOI":"10.1109\/TIP.2023.3328565","volume":"32","author":"X Jiang","year":"2023","unstructured":"Jiang, X., Wang, N., Xin, J., Li, K., Yang, X., Li, J., Wang, X., Gao, X.: Fabnet: Frequency-aware binarized network for single image super-resolution. IEEE Trans. Image Process. 32, 6234\u20136247 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"1501_CR51","first-page":"58","volume":"36","author":"H Qin","year":"2024","unstructured":"Qin, H., Zhang, Y., Ding, Y., Liu, X., Danelljan, M., Yu, F., et al.: Quantsr: accurate low-bit quantization for efficient image super-resolution. Adv. Neural Inf. Process. Syst. 36, 58 (2024)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1501_CR52","doi-asserted-by":"crossref","unstructured":"Li, X., Dong, J., Tang, J., Pan, J.: Dlgsanet: lightweight dynamic local and global self-attention networks for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12792\u201312801 (2023)","DOI":"10.1109\/ICCV51070.2023.01175"},{"key":"1501_CR53","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"1501_CR54","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1501_CR55","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1501_CR56","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\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"1501_CR57","unstructured":"Li, Y., Zhang, Y., Timofte, R., Van\u00a0Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., et al.: Ntire 2023 challenge on efficient super-resolution: Methods and results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1921\u20131959 (2023)"},{"key":"1501_CR58","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01501-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01501-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01501-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:14:34Z","timestamp":1730139274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01501-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":58,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1501"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01501-x","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2024,9,19]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"276"}}