{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T13:22:25Z","timestamp":1778592145860,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073155, 62106088, 62206113"],"award-info":[{"award-number":["62073155, 62106088, 62206113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s12293-022-00385-6","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T19:07:58Z","timestamp":1670526478000},"page":"219-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Efficient automatically evolving convolutional neural network for image denoising"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0750-4749","authenticated-orcid":false,"given":"Fang","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhu","family":"Zhenhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun","family":"Jun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Xiaojun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"385_CR1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"385_CR2","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91\u201399"},{"key":"385_CR3","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\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"385_CR4","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 (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080\u20132095. https:\/\/doi.org\/10.1109\/TIP.2007.901238","journal-title":"IEEE Trans Image Process"},{"key":"385_CR5","doi-asserted-by":"crossref","unstructured":"Anwar S, Barnes N (2019) Real image denoising with feature attention. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3155\u20133164","DOI":"10.1109\/ICCV.2019.00325"},{"key":"385_CR6","doi-asserted-by":"publisher","unstructured":"Gu S, Zhang L, Zuo W, Feng X (2014) Weighted nuclear norm minimization with application to image denoising. In: 2014 IEEE conference on computer vision and pattern recognition, pp 2862\u20132869. https:\/\/doi.org\/10.1109\/CVPR.2014.366","DOI":"10.1109\/CVPR.2014.366"},{"key":"385_CR7","doi-asserted-by":"publisher","unstructured":"Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image restoration. In: 2011 International conference on computer vision, pp 479\u2013486. https:\/\/doi.org\/10.1109\/ICCV.2011.6126278","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"385_CR8","doi-asserted-by":"publisher","unstructured":"Burger HC, Schuler CJ, Harmeling S (2012) Image denoising: Can plain neural networks compete with BM3D? In: 2012 IEEE conference on computer vision and pattern recognition, pp 2392\u20132399. https:\/\/doi.org\/10.1109\/CVPR.2012.6247952","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"385_CR9","doi-asserted-by":"publisher","unstructured":"Schmidt U, Roth S (2014) Shrinkage fields for effective image restoration. In: 2014 IEEE conference on computer vision and pattern recognition, pp 2774\u20132781. https:\/\/doi.org\/10.1109\/CVPR.2014.349","DOI":"10.1109\/CVPR.2014.349"},{"issue":"6","key":"385_CR10","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","volume":"39","author":"Y Chen","year":"2017","unstructured":"Chen Y, Pock T (2017) Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256\u20131272. https:\/\/doi.org\/10.1109\/TPAMI.2016.2596743","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"385_CR11","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 (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142\u20133155. https:\/\/doi.org\/10.1109\/TIP.2017.2662206","journal-title":"IEEE Trans Image Process"},{"key":"385_CR12","doi-asserted-by":"crossref","unstructured":"Zhang H, Li Y, Chen H, Shen C (2020) Memory-efficient hierarchical neural architecture search for image denoising. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3657\u20133666","DOI":"10.1109\/CVPR42600.2020.00371"},{"key":"385_CR13","doi-asserted-by":"crossref","unstructured":"Chen Y-C, Gao C, Robb E, Huang J-B (2020) NAS-DIP: learning deep image prior with neural architecture search. In: Computer vision\u2014ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, proceedings, part XVIII 16, pp 442\u2013459. Springer","DOI":"10.1007\/978-3-030-58523-5_26"},{"key":"385_CR14","doi-asserted-by":"crossref","unstructured":"Liu Y, Sun Y, Xue B, Zhang M (2020) Evolving deep convolutional neural networks for hyperspectral image denoising. In: 2020 International joint conference on neural networks (IJCNN), pp 1\u20138. IEEE","DOI":"10.1109\/IJCNN48605.2020.9207509"},{"key":"385_CR15","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TNNLS.2019.2919608","volume":"31","author":"Y Sun","year":"2019","unstructured":"Sun Y, Xue B, Zhang M, Yen GG (2019) Completely automated CNN architecture design based on blocks. IEEE Trans Neural Netw Learn Syst 31:1242\u20131254","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"385_CR16","doi-asserted-by":"crossref","unstructured":"Xie L, Yuille A (2017) Genetic CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1379\u20131388","DOI":"10.1109\/ICCV.2017.154"},{"key":"385_CR17","unstructured":"Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K (2017) Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436"},{"key":"385_CR18","doi-asserted-by":"crossref","unstructured":"Cai H, Chen T, Zhang W, Yu Y, Wang J (2018) Efficient architecture search by network transformation. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11709"},{"key":"385_CR19","doi-asserted-by":"crossref","unstructured":"Zhong Z, Yan J, Wu W, Shao J, Liu C-L (2018) Practical block-wise neural network architecture generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2423\u20132432","DOI":"10.1109\/CVPR.2018.00257"},{"key":"385_CR20","unstructured":"Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le QV, Kurakin A (2017) Large-scale evolution of image classifiers. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 2902\u20132911. JMLR. org"},{"key":"385_CR21","doi-asserted-by":"crossref","unstructured":"Suganuma M, Shirakawa S, Nagao T (2017) A genetic programming approach to designing convolutional neural network architectures. In: Proceedings of the genetic and evolutionary computation conference, pp 497\u2013504. ACM","DOI":"10.1145\/3071178.3071229"},{"key":"385_CR22","unstructured":"Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578"},{"key":"385_CR23","unstructured":"Baker B, Gupta O, Naik N, Raskar R (2016) Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167"},{"key":"385_CR24","doi-asserted-by":"crossref","unstructured":"Wang B, Sun Y, Xue B, Zhang M (2018) Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1\u20138. IEEE","DOI":"10.1109\/CEC.2018.8477735"},{"issue":"1","key":"385_CR25","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1162\/089892999563184","volume":"11","author":"RS Sutton","year":"1999","unstructured":"Sutton RS, Barto AG (1999) Reinforcement learning. J Cogn Neurosci 11(1):126\u2013134","journal-title":"J Cogn Neurosci"},{"key":"385_CR26","doi-asserted-by":"crossref","unstructured":"Lorenzo PR, Nalepa J, Ramos LS, Pastor JR (2017) Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. In: Proceedings of the genetic and evolutionary computation conference companion, pp 1864\u20131871. ACM","DOI":"10.1145\/3067695.3084211"},{"issue":"11","key":"385_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"385_CR28","doi-asserted-by":"crossref","unstructured":"Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4293\u20134302","DOI":"10.1109\/CVPR.2016.465"},{"key":"385_CR29","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"385_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"02","key":"385_CR31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 6(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl Based Syst"},{"key":"385_CR32","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"385_CR33","unstructured":"Santurkar S, Tsipras D, Ilyas A, Madry A (2018) How does batch normalization help optimization? In: Advances in neural information processing systems, pp 2483\u20132493"},{"key":"385_CR34","doi-asserted-by":"crossref","unstructured":"Sun Y, Xue B, Zhang M., Yen GG (2019) Completely automated CNN architecture design based on blocks. IEEE Trans Neural Netw Learn Syst","DOI":"10.26686\/wgtn.13158305"},{"issue":"8","key":"385_CR35","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1109\/TNNLS.2019.2933879","volume":"31","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Yen GG, Yi Z (2019) Evolutionary compression of deep neural networks for biomedical image segmentation. IEEE Trans Neural Netw Learn Syst 31(8):2916\u20132929","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"385_CR36","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/TEVC.2018.2791283","volume":"23","author":"Y Sun","year":"2019","unstructured":"Sun Y, Yen GG, Yi Z (2019) IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Evol Comput 23(2):173\u2013187","journal-title":"IEEE Trans Evol Comput"},{"key":"385_CR37","unstructured":"Li Y, Yuan Y (2017) Convergence analysis of two-layer neural networks with relu activation. In: Advances in neural information processing systems, pp 597\u2013607"},{"key":"385_CR38","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"385_CR39","doi-asserted-by":"crossref","unstructured":"Koza JR (2007) Introduction to genetic programming. In: Proceedings of the 9th annual conference companion on genetic and evolutionary computation, pp 3323\u20133365","DOI":"10.1145\/1274000.1274116"},{"key":"385_CR40","first-page":"181","volume":"1","author":"T Blickle","year":"2000","unstructured":"Blickle T (2000) Tournament selection. Evol Comput 1:181\u2013186","journal-title":"Evol Comput"},{"issue":"1","key":"385_CR41","doi-asserted-by":"publisher","first-page":"9","DOI":"10.18178\/ijmlc.2017.7.1.611","volume":"7","author":"SM Lim","year":"2017","unstructured":"Lim SM, Sultan ABM, Sulaiman MN, Mustapha A, Leong KY (2017) Crossover and mutation operators of genetic algorithms. Int J Mach Learn Comput 7(1):9\u201312","journal-title":"Int J Mach Learn Comput"},{"key":"385_CR42","doi-asserted-by":"crossref","unstructured":"Lorenzo PR, Nalepa J, Kawulok M, Ramos LS, Pastor JR (2017) Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings of the genetic and evolutionary computation conference, pp 481\u2013488","DOI":"10.1145\/3071178.3071208"},{"key":"385_CR43","doi-asserted-by":"crossref","unstructured":"Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT\u20192010, pp 177\u2013186. Springer","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"385_CR44","unstructured":"Aviris N (2012) Indiana\u2019s Indian Pines 1992 data set"},{"issue":"4","key":"385_CR45","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"385_CR46","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378\u20132386","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"385_CR47","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/LGRS.2012.2193372","volume":"10","author":"D Renza","year":"2013","unstructured":"Renza D, Martinez E, Arquero A (2013) A new approach to change detection in multispectral images by means of ERGAS index. IEEE Geosci Remote Sens Lett 10(1):76\u201380. https:\/\/doi.org\/10.1109\/LGRS.2012.2193372","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-022-00385-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-022-00385-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-022-00385-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T09:22:52Z","timestamp":1687339372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-022-00385-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["385"],"URL":"https:\/\/doi.org\/10.1007\/s12293-022-00385-6","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,8]]},"assertion":[{"value":"5 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}