{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:00Z","timestamp":1740122640531,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["No. 20180550886"],"award-info":[{"award-number":["No. 20180550886"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013099","name":"Scientific Research Fund of Liaoning Provincial Education Department","doi-asserted-by":"publisher","award":["No. JZL202015402"],"award-info":[{"award-number":["No. JZL202015402"]}],"id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation for youth scientists of China","award":["No. 61802161"],"award-info":[{"award-number":["No. 61802161"]}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2020-MS-292"],"award-info":[{"award-number":["2020-MS-292"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04536-1","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T06:02:20Z","timestamp":1683525740000},"page":"20998-21011","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EAdderSR: enhanced AdderSR for single image super resolution"],"prefix":"10.1007","volume":"53","author":[{"given":"Jie","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4260-2715","authenticated-orcid":false,"given":"Huawei","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Wenqian","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"4536_CR1","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision. Springer, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"4536_CR2","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"4536_CR3","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"4536_CR4","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"4536_CR5","unstructured":"Denton E L, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. Adv Neural Inf Process Syst:27"},{"key":"4536_CR6","unstructured":"Song H, Mao H, Dally W J (2016) Deep compression: compressing deep neural networks with pruning trained quantization and huffman coding. In: ICLR"},{"key":"4536_CR7","doi-asserted-by":"crossref","unstructured":"Hou Z, Kung SY (2020) Efficient image super resolution via channel discriminative deep neural network pruning","DOI":"10.1109\/ICASSP40776.2020.9054019"},{"key":"4536_CR8","unstructured":"Guo Y, Yao A, Chen Y (2016) Dynamic network surgery for efficient dnns. Adv Neural Inf Process Syst:29"},{"key":"4536_CR9","doi-asserted-by":"crossref","unstructured":"Wang H, Gui S, Yang H, Liu J, Wang Z (2020) Gan slimming: all-in-one gan compression by a unified optimization framework. In: European conference on computer vision. Springer, pp 54\u201373","DOI":"10.1007\/978-3-030-58548-8_4"},{"key":"4536_CR10","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 (2021) Learning lightweight super-resolution networks with weight pruning. Neural Netw 144:21\u201332","journal-title":"Neural Netw"},{"issue":"7","key":"4536_CR11","first-page":"38","volume":"14","author":"G Hinton","year":"2015","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Comput Sci 14(7):38\u201339","journal-title":"Comput Sci"},{"key":"4536_CR12","doi-asserted-by":"crossref","unstructured":"Gao Q, Zhao Y, Li G, Tong T (2018) Image super-resolution using knowledge distillation. In: Asian conference on computer vision. Springer, pp 527\u2013541","DOI":"10.1007\/978-3-030-20890-5_34"},{"key":"4536_CR13","unstructured":"Fu Y, Chen W, Wang H, Li H, Lin Y, Wang Z (2020) Autogan-distiller: searching to compress generative adversarial networks. JMLR.org, ICML\u201920"},{"key":"4536_CR14","first-page":"12322","volume":"33","author":"Y Xu","year":"2020","unstructured":"Xu Y, Xu C, Chen X, Zhang W, Xu C, Wang Y (2020) Kernel based progressive distillation for adder neural networks. Adv Neural Inf Process Syst 33:12322\u201312333","journal-title":"Adv Neural Inf Process Syst"},{"key":"4536_CR15","doi-asserted-by":"crossref","unstructured":"Gao G, Li W, Li J, Wu F, Lu H, Yu Y (2022) Feature distillation interaction weighting network for lightweight image super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 661-669","DOI":"10.1609\/aaai.v36i1.19946"},{"key":"4536_CR16","unstructured":"Courbariaux M, Bengio Y, David J P (2015) Binaryconnect: training deep neural networks with binary weights during propagations. Adv Neural Inf Process Syst:28"},{"key":"4536_CR17","unstructured":"Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks. Adv Neural Inf Process Syst :29"},{"key":"4536_CR18","doi-asserted-by":"crossref","unstructured":"Ma Y, Xiong H, Hu Z, Ma L (2019) Efficient super resolution using binarized neural network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 0\u20130","DOI":"10.1109\/CVPRW.2019.00096"},{"key":"4536_CR19","doi-asserted-by":"crossref","unstructured":"Li H, Yan C, Lin S, Zheng X, Zhang B, Yang F, Ji R (2020) Pams: quantized super-resolution via parameterized max scale. In: European conference on computer vision. Springer, pp 564\u2013580","DOI":"10.1007\/978-3-030-58595-2_34"},{"key":"4536_CR20","doi-asserted-by":"crossref","unstructured":"Xin J, Wang N, Jiang X, Li J, Huang H, Gao X (2020 ) Binarized neural network for single image super resolution. In: European conference on computer vision. Springer, pp 91\u2013107","DOI":"10.1007\/978-3-030-58548-8_6"},{"key":"4536_CR21","unstructured":"Liu Z, Shen Z, Li S, Helwegen K, Huang D, Cheng KT (2021a) How do adam and training strategies help bnns optimization. In: International conference on machine learning. PMLR, pp 6936\u20136946"},{"key":"4536_CR22","doi-asserted-by":"crossref","unstructured":"Liu C, Ding W, Hu Y, Zhang B, Liu J, Guo G, Doermann D (2021b) Rectified binary convolutional networks with generative adversarial learning. Int J Comput Vis 129(4):998\u20131012","DOI":"10.1007\/s11263-020-01417-9"},{"key":"4536_CR23","doi-asserted-by":"publisher","first-page":"108962","DOI":"10.1016\/j.knosys.2022.108962","volume":"249","author":"T Gao","year":"2022","unstructured":"Gao T, Zhou Y, Duan S, Hu X (2022) Memristive kdg-bnn: memristive binary neural networks trained via knowledge distillation and generative adversarial networks. Knowl-Based Syst 249:108962","journal-title":"Knowl-Based Syst"},{"key":"4536_CR24","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Y, Xu C, Shi B, Xu C, Tian Q, Xu C (2020) Addernet: do we really need multiplications in deep learning?. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1468\u20131477","DOI":"10.1109\/CVPR42600.2020.00154"},{"key":"4536_CR25","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci"},{"key":"4536_CR26","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Inf Process Syst:25"},{"key":"4536_CR27","doi-asserted-by":"crossref","unstructured":"Song D, Wang Y, Chen H, Xu C, Xu C, Tao D (2021) Addersr: towards energy efficient image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15648\u201315657","DOI":"10.1109\/CVPR46437.2021.01539"},{"key":"4536_CR28","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision,. Springer, pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"4536_CR29","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR, pp 448\u2013456"},{"key":"4536_CR30","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"4536_CR31","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"4536_CR32","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"4536_CR33","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn KA (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European conference on computer vision (ECCV), pp 252\u2013268","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"4536_CR34","doi-asserted-by":"crossref","unstructured":"Hui Z, Wang X, Gao X (2018) Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 723\u2013731","DOI":"10.1109\/CVPR.2018.00082"},{"key":"4536_CR35","doi-asserted-by":"crossref","unstructured":"Muqeet A, Hwang J, Yang S, Kang JH, Kim Y, Bae SH (2020) Ultra lightweight image super-resolution with multi-attention layers, vol 2(5)","DOI":"10.1007\/978-3-030-67070-2_6"},{"key":"4536_CR36","doi-asserted-by":"crossref","unstructured":"Zeng C, Li G, Chen Q, Xiao Q (2022) Lightweight global-locally connected distillation network for single image super-resolution. Appl Intell:1\u201313","DOI":"10.1007\/s10489-022-03454-y"},{"key":"4536_CR37","unstructured":"Romero A, Ballas N, Kahou S E, Chassang A, Gatta C, Bengio Y (2015) Fitnets: hints for thin deep nets. Computer ence"},{"key":"4536_CR38","doi-asserted-by":"crossref","unstructured":"Shen L, Ziaeefard M, Meyer B, Gross W, Clark JJ (2022) Conjugate adder net (caddnet)-a space-efficient approximate cnn. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2793\u20132797","DOI":"10.1109\/CVPRW56347.2022.00316"},{"key":"4536_CR39","doi-asserted-by":"crossref","unstructured":"Li W, Chen X, Bai J, Ning X, Wang Y (2022) Searching for energy-efficient hybrid adder-convolution neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1943\u20131952","DOI":"10.1109\/CVPRW56347.2022.00211"},{"key":"4536_CR40","unstructured":"You H, Li B, Huihong S, Fu Y, Lin Y (2022) Shiftaddnas: hardware-inspired search for more accurate and efficient neural networks. In: International conference on machine learning. PMLR, pp 25566\u201325580"},{"key":"4536_CR41","doi-asserted-by":"crossref","unstructured":"Elhoushi M, Chen Z, Shafiq F, Tian YH, Li JY (2021) Deepshift: towards multiplication-less neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2359\u20132368","DOI":"10.1109\/CVPRW53098.2021.00268"},{"key":"4536_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013 778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4536_CR43","unstructured":"Oliveira NAPd et al (2018) Single image super-resolution method based on linear regression and box-cox transformation"},{"issue":"3","key":"4536_CR44","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1117\/12.242618","volume":"5","author":"G Ramponi","year":"1996","unstructured":"Ramponi G, Strobel N K, Mitra S K, Yu T H (1996) Nonlinear unsharp masking methods for image contrast enhancement. J Electr Imaging 5(3):353\u2013366","journal-title":"J Electr Imaging"},{"key":"4536_CR45","doi-asserted-by":"crossref","unstructured":"Agustsson E, Timofte R (2017) 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","DOI":"10.1109\/CVPRW.2017.150"},{"key":"4536_CR46","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. Comput Sci"},{"key":"4536_CR47","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"4536_CR48","doi-asserted-by":"crossref","unstructured":"Han D (2013) Comparison of commonly used image interpolation methods. In: Conference of the 2nd international conference on computer science and electronics engineering (ICCSEE 2013). Atlantis Press, pp 1556\u20131559","DOI":"10.2991\/iccsee.2013.391"},{"key":"4536_CR49","doi-asserted-by":"crossref","unstructured":"Horowitz M (2014) Computing\u2019s energy problem (and what we can do about it). In: 2014 IEEE international solid- state circuits conference (ISSCC)","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"4536_CR50","doi-asserted-by":"crossref","unstructured":"Sze V, Chen Y H, Yang T J, Emer J S (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE, vol 105(12)","DOI":"10.1109\/JPROC.2017.2761740"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04536-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04536-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04536-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:11:15Z","timestamp":1695121875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04536-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":50,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4536"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04536-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,5,8]]},"assertion":[{"value":"16 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}