{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:14:12Z","timestamp":1760235252811,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876067"],"award-info":[{"award-number":["61876067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>License plate image reconstruction plays an important role in Intelligent Transportation Systems. In this paper, a super-resolution image reconstruction method based on Generative Adversarial Networks (GAN) is proposed. The proposed method mainly consists of four parts: (1) pretreatment for the input image; (2) image features extraction using residual dense network; (3) introduction of progressive sampling, which can provide larger receptive field and more information details; (4) discriminator based on markovian discriminator (PatchGAN) can make a more accurate judgment, which guides the generator to reconstruct images with higher quality and details. Regarding the Chinese City Parking Dataset (CCPD) dataset, compared with the current better algorithm, the experiment results prove that our model has a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and less reconstruction time, which verifies the feasibility of our approach.<\/jats:p>","DOI":"10.3390\/rs13153018","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T21:44:32Z","timestamp":1627854272000},"page":"3018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["License Plate Image Reconstruction Based on Generative Adversarial Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Mianfen","family":"Lin","sequence":"first","affiliation":[{"name":"School of Software, South China Normal University, Foshan 528225, China"}]},{"given":"Liangxin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Foshan 528225, China"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Foshan 528225, China"}]},{"given":"Jingcong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Foshan 528225, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7576-6743","authenticated-orcid":false,"given":"Jiahui","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Software, South China Normal University, Foshan 528225, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1109\/TIP.2012.2189576","article-title":"Interpolation-based image super-resolution using multisurface fitting","volume":"21","author":"Zhou","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TPAMI.2004.1261081","article-title":"Fundamental limits of reconstructionbased superresolution algorithms under local translation","volume":"26","author":"Lin","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","first-page":"920","article-title":"Image super-resolution algorithms based on sparse representation of classified image patches","volume":"40","author":"Lian","year":"2012","journal-title":"Dianzi Xuebao"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, K.J., and Lee, K.M. (July, January 26). Accurate image super-resolution using very deep convolutional networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, K.J., and Lee, K.M. (July, January 26). Deeply-recursive convolutional network for image super-resolution. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_7","first-page":"2472","article-title":"Residual dense network for image super-resolution","volume":"43","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","first-page":"1","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","volume":"11","author":"Ledig","year":"2016","journal-title":"arXiv e-prints"},{"key":"ref_9","first-page":"1977","article-title":"Image super-resolution based on generative adversarial networks: A brief review","volume":"64","author":"Fu","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_10","unstructured":"Wang, X.T., Ke, Y., Wu, S.X., Gu, J.J., Liu, Y.H., Chao, D., Yu, Q., and Chen, C.L. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the 2018 European Conference on Computer Vision workshops, Munich, Germany."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4367","DOI":"10.1007\/s10489-020-02116-1","article-title":"Image super-resolution reconstruction based on feature map attention mechanism","volume":"51","author":"Chen","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1007\/s11036-020-01681-6","article-title":"An image super-resolution reconstruction method with single frame character based on wavelet neural network in internet of things","volume":"26","author":"Guo","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_13","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Bing, X., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 2014 Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ratliff, L.J., Burden, S.A., and Sastry, S.S. (2013, January 2\u20134). Characterization and computation of local Nash equilibria in continuous games. Proceedings of the 2013 Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA.","DOI":"10.1109\/Allerton.2013.6736623"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1007\/s11517-020-02262-1","article-title":"A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact","volume":"58","author":"Lai","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 22\u201325). Image-to-image translation with conditional adversarial networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wei, Y., Meng, A., Lu, N., Huang, H., Ying, C., and Huang, L. (2018, January 8\u201314). Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline. Proceedings of the 2018 European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_16"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"2003","journal-title":"IEEE Trans. Acoust."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a deep convolutional network for image super-resolution. Proceedings of the 2014 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1587\/transinf.2019EDL8170","article-title":"Multi-targeted backdoor: Indentifying backdoor attack for multiple deep neural networks","volume":"103","author":"Kwon","year":"2020","journal-title":"IEICE Trans. Inf. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:38:45Z","timestamp":1760164725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,1]]},"references-count":21,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13153018"],"URL":"https:\/\/doi.org\/10.3390\/rs13153018","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,8,1]]}}}