{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:27:53Z","timestamp":1767652073391,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09283-5","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T16:02:20Z","timestamp":1702310540000},"page":"4281-4291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Film-GAN: towards realistic analog film photo generation"],"prefix":"10.1007","volume":"36","author":[{"given":"Haoyan","family":"Gong","sequence":"first","affiliation":[]},{"given":"Jionglong","family":"Su","sequence":"additional","affiliation":[]},{"given":"Kah Phooi","family":"Seng","sequence":"additional","affiliation":[]},{"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Ao","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7398-9193","authenticated-orcid":false,"given":"Hongbin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"9283_CR1","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et\u00a0al. (2014) Generative adversarial nets. Advances in neural information processing systems 27"},{"key":"9283_CR2","doi-asserted-by":"crossref","unstructured":"Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2016.265"},{"key":"9283_CR3","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":"9283_CR4","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, et\u00a0al. (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"9283_CR5","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, et\u00a0al. (2020) Stargan v2: Diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"9283_CR6","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE international conference on computer vision, pp 1501\u20131510","DOI":"10.1109\/ICCV.2017.167"},{"key":"9283_CR7","unstructured":"Li Y, Fang C, Yang J, et\u00a0al. (2017) Universal style transfer via feature transforms. Advances in neural information processing systems 30"},{"key":"9283_CR8","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, et\u00a0al. (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"9283_CR9","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"9283_CR10","unstructured":"Xu Z, Wilber M, Fang C, et\u00a0al. (2018) Learning from multi-domain artistic images for arbitrary style transfer. arXiv preprint arXiv:1805.09987"},{"key":"9283_CR11","doi-asserted-by":"crossref","unstructured":"Sanakoyeu A, Kotovenko D, Lang S, et\u00a0al. (2018) A style-aware content loss for real-time hd style transfer. In: proceedings of the European conference on computer vision (ECCV), pp 698\u2013714","DOI":"10.1007\/978-3-030-01237-3_43"},{"key":"9283_CR12","unstructured":"Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. Advances in neural information processing systems 30"},{"key":"9283_CR13","doi-asserted-by":"crossref","unstructured":"Yi Z, Zhang H, Tan P, et\u00a0al. (2017) Dualgan: Unsupervised dual learning for image-to-image translation. In: Proceedings of the IEEE international conference on computer vision, pp 2849\u20132857","DOI":"10.1109\/ICCV.2017.310"},{"key":"9283_CR14","unstructured":"Kim T, Cha M, Kim H, et\u00a0al. (2017) Learning to discover cross-domain relations with generative adversarial networks. In: International conference on machine learning, PMLR, pp 1857\u20131865"},{"key":"9283_CR15","doi-asserted-by":"crossref","unstructured":"Park T, Efros AA, Zhang R, et\u00a0al. (2020) Contrastive learning for unpaired image-to-image translation. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IX 16, Springer, pp 319\u2013345","DOI":"10.1007\/978-3-030-58545-7_19"},{"key":"9283_CR16","unstructured":"Tang H, Liu H, Xu D, et\u00a0al. (2021) Attentiongan: Unpaired image-to-image translation using attention-guided generative adversarial networks. IEEE Transactions on Neural Networks and Learning Systems"},{"key":"9283_CR17","unstructured":"Back J (2021) Fine-tuning stylegan2 for cartoon face generation. arXiv preprint arXiv:2106.12445"},{"key":"9283_CR18","unstructured":"Yu Y, Kamran G, HsiangTao W, et\u00a0al. (2022) Expanding the latent space of stylegan for real face editing. arXiv preprint arXiv:2204.12530"},{"key":"9283_CR19","doi-asserted-by":"crossref","unstructured":"Hou X, Song J, Liu H (2022) Unpaired image-to-image translation using generative adversarial networks with coordinate attention loss. In: 2022 4th International Conference on Intelligent Information Processing (IIP), IEEE, pp 68\u201376","DOI":"10.1109\/IIP57348.2022.00021"},{"key":"9283_CR20","unstructured":"Zaino G, Recchiuto CT, Sgorbissa A (2022) Culture-to-culture image translation with generative adversarial networks. arXiv preprint arXiv:2201.01565"},{"key":"9283_CR21","unstructured":"Song J, Yi H, Xu W, et\u00a0al. (2022) Dual perceptual loss for single image super-resolution using esrgan. arXiv preprint arXiv:2201.06383"},{"key":"9283_CR22","doi-asserted-by":"crossref","unstructured":"Dalva Y, Alt\u0131ndi\u015f SF, Dundar A (2022) Vecgan: Image-to-image translation with interpretable latent directions. In: European Conference on Computer Vision, Springer, pp 153\u2013169","DOI":"10.1007\/978-3-031-19787-1_9"},{"key":"9283_CR23","doi-asserted-by":"crossref","unstructured":"Huang X, Liu MY, Belongie S, et\u00a0al. (2018) Multimodal unsupervised image-to-image translation. In: Proceedings of the European conference on computer vision (ECCV), pp 172\u2013189","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"9283_CR24","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, et\u00a0al. (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789\u20138797","DOI":"10.1109\/CVPR.2018.00916"},{"key":"9283_CR25","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1109\/TMM.2021.3065230","volume":"24","author":"J Huang","year":"2021","unstructured":"Huang J, Liao J, Kwong S (2021) Unsupervised image-to-image translation via pre-trained stylegan2 network. IEEE Trans. Multimed 24:1435\u20131448","journal-title":"IEEE Trans. Multimed"},{"issue":"3","key":"9283_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102926","volume":"59","author":"Z Qin","year":"2022","unstructured":"Qin Z, Chen Q, Ding Y et al (2022) Segmentation mask and feature similarity loss guided gan for object-oriented image-to-image translation. Inform Process Manag 59(3):102,926","journal-title":"Inform Process Manag"},{"issue":"5","key":"9283_CR27","first-page":"6055","volume":"45","author":"H Tang","year":"2022","unstructured":"Tang H, Torr PH, Sebe N (2022) Multi-channel attention selection gans for guided image-to-image translation. IEEE Trans Pattern Anal Mach Intell 45(5):6055\u20136071","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9283_CR28","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhang H, Yang K, et\u00a0al. (2022) Learning to synthesize compatible fashion items using semantic alignment and collocation classification: An outfit generation framework. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2022.3202842"},{"issue":"106","key":"9283_CR29","first-page":"196","volume":"123","author":"K Hu","year":"2023","unstructured":"Hu K, Weng C, Shen C et al (2023) A multi-stage underwater image aesthetic enhancement algorithm based on a generative adversarial network. Eng Appl Artificial Intell 123(106):196","journal-title":"Eng Appl Artificial Intell"},{"key":"9283_CR30","doi-asserted-by":"crossref","unstructured":"Wang N, Chen T, Kong X, et\u00a0al. (2023) Underwater attentional generative adversarial networks for image enhancement. IEEE Transactions on Human-Machine Systems","DOI":"10.1109\/THMS.2023.3261341"},{"key":"9283_CR31","unstructured":"Zhu JY, Zhang R, Pathak D, et\u00a0al. (2017) Toward multimodal image-to-image translation. Advances in neural information processing systems 30"},{"key":"9283_CR32","doi-asserted-by":"crossref","unstructured":"Lee HY, Tseng HY, Huang JB, et\u00a0al. (2018) Diverse image-to-image translation via disentangled representations. In: Proceedings of the European conference on computer vision (ECCV), pp 35\u201351","DOI":"10.1007\/978-3-030-01246-5_3"},{"issue":"10","key":"9283_CR33","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1007\/s11263-019-01284-z","volume":"128","author":"HY Lee","year":"2020","unstructured":"Lee HY, Tseng HY, Mao Q et al (2020) Drit++: Diverse image-to-image translation via disentangled representations. Int J Comput Vis 128(10):2402\u20132417","journal-title":"Int J Comput Vis"},{"key":"9283_CR34","doi-asserted-by":"crossref","unstructured":"Lian Y, Shi X, Shen S, et\u00a0al. (2023) Multitask learning for image translation and salient object detection from multimodal remote sensing images. The Visual Computer pp 1\u201320","DOI":"10.1007\/s00371-023-02857-3"},{"key":"9283_CR35","doi-asserted-by":"crossref","unstructured":"Cao B, Bi Z, Hu Q, et\u00a0al. (2023) Autoencoder-driven multimodal collaborative learning for medical image synthesis. Int J Comput Vis pp 1\u201320","DOI":"10.1007\/s11263-023-01791-0"},{"issue":"2","key":"9283_CR36","doi-asserted-by":"publisher","first-page":"pbac011","DOI":"10.1093\/pcmedi\/pbac011","volume":"5","author":"C Tan","year":"2022","unstructured":"Tan C, Yang M, You Z et al (2022) A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose ct denoising. Precision Clin Med 5(2):pbac011","journal-title":"Precision Clin Med"},{"key":"9283_CR37","doi-asserted-by":"crossref","unstructured":"Wang Y, Chen Y, Wang W, et\u00a0al. (2022) Msgan: Multi-stage generative adversarial networks for cross-modality domain adaptation. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, pp 520\u2013524","DOI":"10.1109\/EMBC48229.2022.9871048"},{"key":"9283_CR38","doi-asserted-by":"crossref","unstructured":"Liu P, Zhang H, Zhang K, et\u00a0al. (2018) Multi-level wavelet-cnn for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 773\u2013782","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"9283_CR39","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9283_CR40","unstructured":"Edwards T (1991) Discrete wavelet transforms: Theory and implementation. Universidad de pp 28\u201335"},{"issue":"10","key":"9283_CR41","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","volume":"40","author":"MJ Shensa","year":"1992","unstructured":"Shensa MJ et al (1992) The discrete wavelet transform: wedding the a trous and mallat algorithms. IEEE Trans Signal Process 40(10):2464\u20132482","journal-title":"IEEE Trans Signal Process"},{"issue":"7","key":"9283_CR42","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 et al (2017) Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142\u20133155","journal-title":"IEEE Trans Image Process"},{"key":"9283_CR43","doi-asserted-by":"crossref","unstructured":"Abdelhamed A, Lin S, Brown MS (2018) A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1692\u20131700","DOI":"10.1109\/CVPR.2018.00182"},{"key":"9283_CR44","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et\u00a0al. (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, Ieee, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9283_CR45","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"2","key":"9283_CR46","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s11263-021-01557-6","volume":"130","author":"Q Mao","year":"2022","unstructured":"Mao Q, Tseng HY, Lee HY et al (2022) Continuous and diverse image-to-image translation via signed attribute vectors. Int J Comput Vis 130(2):517\u2013549","journal-title":"Int J Comput Vis"},{"key":"9283_CR47","unstructured":"Kwon G, Ye JC (2022) Diffusion-based image translation using disentangled style and content representation. arXiv preprint arXiv:2209.15264"},{"key":"9283_CR48","unstructured":"Salimans T, Goodfellow I, Zaremba W, et\u00a0al. (2016) Improved techniques for training gans. Adv Neural Inform Process Syst 29"},{"key":"9283_CR49","unstructured":"Heusel M, Ramsauer H, Unterthiner T, et\u00a0al. (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inform Process Syst 30"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09283-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09283-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09283-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:13:19Z","timestamp":1707732799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09283-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":49,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9283"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09283-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,12,11]]},"assertion":[{"value":"16 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2023","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 relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}