{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:28Z","timestamp":1740107848002,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62072250","61772281"],"award-info":[{"award-number":["No.62072250","61772281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702235","U1636117"],"award-info":[{"award-number":["61702235","U1636117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1804263","62172435"],"award-info":[{"award-number":["U1804263","62172435"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872203","61802212"],"award-info":[{"award-number":["61872203","61802212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhongyuan Science and Technology Innovation Leading Talent Project of China","award":["No.214200510019"],"award-info":[{"award-number":["No.214200510019"]}]},{"name":"Plan for Scientific Talent of Henan Province","award":["No.2018JR0018"],"award-info":[{"award-number":["No.2018JR0018"]}]},{"name":"Opening Project of Guangdong Provincial Key Laboratory of Information Security Technology","award":["No.2020B1212060078"],"award-info":[{"award-number":["No.2020B1212060078"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00530-022-00953-3","type":"journal-article","created":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T13:02:45Z","timestamp":1653570165000},"page":"1809-1822","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ESGAN for generating high quality enhanced samples"],"prefix":"10.1007","volume":"28","author":[{"given":"Junfeng","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9366-5671","authenticated-orcid":false,"given":"Jinwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"953_CR1","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, L., Guo, S., Lai, Z.: Wavelet integrated cnns for noise-robust image classification. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7243\u20137252 (2020)","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"953_CR2","doi-asserted-by":"crossref","unstructured":"Xie, X., Zhou, Y., Kung, S.-Y.: Exploring highly efficient compact neural networks for image classification. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2930\u20132934 (2020)","DOI":"10.1109\/ICIP40778.2020.9191334"},{"key":"953_CR3","doi-asserted-by":"publisher","first-page":"2810","DOI":"10.1109\/TIP.2021.3055613","volume":"30","author":"H Sun","year":"2021","unstructured":"Sun, H., Zheng, X., Lu, X.: A supervised segmentation network for hyperspectral image classification. IEEE Trans. Image Process. 30, 2810\u20132825 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"953_CR4","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1109\/TIP.2020.3043128","volume":"30","author":"X Li","year":"2021","unstructured":"Li, X., Wu, J., Sun, Z., Ma, Z., Cao, J., Xue, J.-H.: Bsnet: Bi-similarity network for few-shot fine-grained image classification. IEEE Trans. Image Process. 30, 1318\u20131331 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"953_CR5","doi-asserted-by":"crossref","unstructured":"Xie, C., Wu, Y., Maaten, L.v.d., Yuille, A.L., He, K.: Feature denoising for improving adversarial robustness. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00059"},{"key":"953_CR6","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E., Ghaoui, L.E., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 7472\u20137482 (2019)"},{"key":"953_CR7","unstructured":"Jin, C., Rinard, M.: Manifold regularization for locally stable deep neural networks. arXiv preprint arXiv:2003.04286 (2020)"},{"key":"953_CR8","doi-asserted-by":"crossref","unstructured":"Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A.L., Le, Q.V.: Adversarial examples improve image recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 816\u2013825 (2020)","DOI":"10.1109\/CVPR42600.2020.00090"},{"key":"953_CR9","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"953_CR10","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"953_CR11","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"953_CR12","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"953_CR13","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 Ieee Symposium on Security and Privacy (sp), pp. 39\u201357 (2017). IEEE","DOI":"10.1109\/SP.2017.49"},{"key":"953_CR14","doi-asserted-by":"crossref","unstructured":"Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. arXiv preprint arXiv:1801.02610 (2018)","DOI":"10.24963\/ijcai.2018\/543"},{"key":"953_CR15","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"953_CR16","doi-asserted-by":"crossref","unstructured":"Mangla, P., Jandial, S., Varshney, S., Balasubramanian, V.N.: Advgan++: Harnessing latent layers for adversary generation. arXiv preprint arXiv:1908.00706 (2019)","DOI":"10.1109\/ICCVW.2019.00257"},{"key":"953_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, Y., Dong, C., Qiao, Y.: Ranksrgan: Generative adversarial networks with ranker for image super-resolution. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3096\u20133105 (2019)","DOI":"10.1109\/ICCV.2019.00319"},{"key":"953_CR18","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, S., Xia, Z., Li, Q., Ma, B., Li, J., Yang, M., Shi, Y.-Q.: Medical image super-resolution via deep residual neural network in the shearlet domain 80, 26637\u201326655 (2021)","DOI":"10.1007\/s11042-021-10894-0"},{"key":"953_CR19","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G.: Residual feature aggregation network for image super-resolution. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2356\u20132365 (2020)","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"953_CR20","doi-asserted-by":"publisher","first-page":"6648","DOI":"10.1109\/TIP.2021.3096089","volume":"30","author":"Q Wang","year":"2021","unstructured":"Wang, Q., Gao, Q., Wu, L., Sun, G., Jiao, L.: Adversarial multi-path residual network for image super-resolution. IEEE Trans. Image Process. 30, 6648\u20136658 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"953_CR21","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184\u2013199 (2014). Springer","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"953_CR22","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"953_CR23","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 Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"953_CR24","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 Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"953_CR25","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: 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 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"953_CR26","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C.C., Qiao, Y., Tang, X.: Esrgan: Enhanced super-resolution generative adversarial networks. European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"953_CR27","doi-asserted-by":"crossref","unstructured":"Rakotonirina, N.C., Rasoanaivo, A.: Esrgan+ : Further improving enhanced super-resolution generative adversarial network. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3637\u20133641 (2020)","DOI":"10.1109\/ICASSP40776.2020.9054071"},{"issue":"6","key":"953_CR28","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"953_CR29","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"953_CR30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"953_CR31","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"953_CR32","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3498\u20133505 (2012). IEEE","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"953_CR33","first-page":"18613","volume":"33","author":"ED Cubuk","year":"2020","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.: Randaugment: Practical automated data augmentation with a reduced search space. Adv. Neural. Inf. Process. Syst. 33, 18613\u201318624 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"953_CR34","first-page":"6256","volume":"33","author":"Q Xie","year":"2020","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. Adv. Neural. Inf. Process. Syst. 33, 6256\u20136268 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"953_CR35","unstructured":"Berthelot, D., Carlini, N., Cubuk, E.D., Kurakin, A., Sohn, K., Zhang, H., Raffel, C.: Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring. In: International Conference on Learning Representations (2020)"},{"key":"953_CR36","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2017)","DOI":"10.5244\/C.30.87"},{"key":"953_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"953_CR38","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15262\u201315271 (2021)","DOI":"10.1109\/CVPR46437.2021.01501"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00953-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-022-00953-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00953-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T12:29:39Z","timestamp":1664108979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-022-00953-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":38,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["953"],"URL":"https:\/\/doi.org\/10.1007\/s00530-022-00953-3","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2022,5,26]]},"assertion":[{"value":"18 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}