{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:09:23Z","timestamp":1776100163325,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"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":["62172231"],"award-info":[{"award-number":["62172231"]}],"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":["BK20220107"],"award-info":[{"award-number":["BK20220107"]}],"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":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}],"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":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province of China","doi-asserted-by":"publisher","award":["62172231"],"award-info":[{"award-number":["62172231"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province of China","doi-asserted-by":"publisher","award":["BK20220107"],"award-info":[{"award-number":["BK20220107"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province of China","doi-asserted-by":"publisher","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province of China","doi-asserted-by":"publisher","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["62172231"],"award-info":[{"award-number":["62172231"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["BK20220107"],"award-info":[{"award-number":["BK20220107"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62172231"],"award-info":[{"award-number":["62172231"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["BK20220107"],"award-info":[{"award-number":["BK20220107"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2020LZH005"],"award-info":[{"award-number":["ZR2020LZH005"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M713668"],"award-info":[{"award-number":["2022M713668"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the latent mapping of two domains is also one of the factors affecting the performance of existing methods. To make the data schema stable, this paper develops a novel binary noise guidance learning (BnGLGAN) framework for image translation to solve these problems. Specifically, to eliminate uncertainty of domain distribution, a noise prior inference learning (NPIL) module is designed to infer an estimated distribution from a certain domain. In addition, to improve the authenticity of reconstructed images, a distribution-guided noise reconstruction learning (DgNRL) module is introduced to reconstruct the noise from the source domain, which can provide source semantic information to guide the GAN\u2019s generation. Extensive experiments fully prove the efficiency of our proposed framework and its advantages over comparable methods.<\/jats:p>","DOI":"10.3390\/rs16010065","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8741-8607","authenticated-orcid":false,"given":"Guoqing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Ruixin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-3800","authenticated-orcid":false,"given":"Yuhui","family":"Zheng","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China"}]},{"given":"Baozhu","family":"Li","sequence":"additional","affiliation":[{"name":"Internet of Things & Smart City Innovation Platform, Zhuhai Fudan Innovation Institute, Zhuhai 519031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A. (2017, January 21\u201326). Image-To-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126654","DOI":"10.1016\/j.neucom.2023.126654","article-title":"Caster: Cartoon style transfer via dynamic cartoon style casting","volume":"556","author":"Zhang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., and Efros, A. (2016, January 11\u201314). Colorful image colorization. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part III 14.","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1109\/JSTARS.2022.3197748","article-title":"Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer","volume":"15","author":"Zhao","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9176","DOI":"10.1109\/JSTARS.2021.3109600","article-title":"Progressive data augmentation method for remote sensing ship image classification based on imaging simulation system and neural style transfer","volume":"14","author":"Xiao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8257","DOI":"10.1109\/TGRS.2020.3042507","article-title":"Recurrent thrifty attention network for remote sensing scene recognition","volume":"59","author":"Fu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Merugu, S., Jain, K., and Mittal, A. (2020). ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications, Springer."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, G., Ge, Y., and Dong, Z. (2021). Deep high-resolution representation learning for cross-resolution person re-identification. IEEE Trans. Image Process., 8913\u20138925.","DOI":"10.1109\/TIP.2021.3120054"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3818","DOI":"10.1109\/TNNLS.2019.2944869","article-title":"Nonpeaked discriminant analysis for data representation","volume":"30","author":"Ye","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, J., Shoeiby, M., and Malthus, T. (2021, January 11\u201316). Single underwater image restoration by contrastive learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553857"},{"key":"ref_11","unstructured":"Skandha, S., Saba, L., and Gupta, S. (2022). Multimodality Imaging, Volume 1: Deep Learning Applications, IOP Publishing."},{"key":"ref_12","unstructured":"Saba, L., Skandha, S., and Gupta, S. (2022). Multimodality Imaging, Volume 1: Deep Learning Applications, IOP Publishing."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, G., Fang, W., and Zheng, Y. (2023). SDBAD-Net: A Spatial Dual-Branch Attention Dehazing Network based on Meta-Former Paradigm. IEEE Trans. Circuits Syst. Video Technol.","DOI":"10.1109\/TCSVT.2023.3274366"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, J., and Chao, H. (2018, January 18\u201323). Image blind denoising with generative adversarial network based noise modeling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00333"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ian, G., Jean, P., and Mehdi, M. (2020). Generative adversarial networks. Commun. ACM, 139\u2013144.","DOI":"10.1145\/3422622"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2021.07.037","article-title":"Zstgan: An adversarial approach for unsupervised zero-shot image-to-image translation","volume":"461","author":"Lin","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3859","DOI":"10.1109\/TMM.2021.3109419","article-title":"Image-to-image translation: Methods and applications","volume":"24","author":"Pang","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, G., Liu, J., and Chen, Y. (2023). Multi-biometric unified network for cloth-changing person re-identification. IEEE Trans. Image Process., 4555\u20134566.","DOI":"10.1109\/TIP.2023.3279673"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1109\/TCSVT.2023.3240001","article-title":"Camera contrast learning for unsupervised person re-identification","volume":"33","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8599","DOI":"10.1109\/TCSVT.2022.3194084","article-title":"Global relation-aware contrast learning for unsupervised person re-identification","volume":"32","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6766","DOI":"10.1109\/TCSVT.2022.3169422","article-title":"Illumination unification for person re-identification","volume":"32","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., and Tan, P. (2017, January 22\u201329). Dualgan: Unsupervised dual learning for image-to-image translation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref_23","unstructured":"Che, T., Li, Y., and Jacob, A. (2016). Mode regularized generative adversarial networks. arXiv."},{"key":"ref_24","unstructured":"Srivastava, A., Valkov, L., Russell, C., Gutmann, M., and Sutton, C. (2021, January 11\u201317). Veegan: Reducing mode collapse in gans using implicit variational learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bang, D., and Shim, H. (2021, January 10\u201317). Mggan: Solving mode collapse using manifold-guided training. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00266"},{"key":"ref_26","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","volume":"28","author":"Sohn","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., and Ye, Q. (2018, January 18\u201323). Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00110"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Richardson, E., Alaluf, Y., and Patashnik, O. (2021, January 20\u201325). Encoding in style: A stylegan encoder for image-to-image translation. Proceedings of the IEEE\/CVF Conference on Computer vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, B., and Zhang, T. (2021, January 20\u201325). Cocosnet v2: Full-resolution correspondence learning for image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01130"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mustafa, A., and Mantiuk, R. (2020, January 23\u201328). Transformation consistency regularization\u2013a semi-supervised paradigm for image-to-image translation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XVIII 16.","DOI":"10.1007\/978-3-030-58523-5_35"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., and Zhang, L. (2018, January 18\u201323). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lee, H., Tseng, H., and Huang, J. (2018, January 8\u201314). Diverse image-to-image translation via disentangled representations. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, T., Liu, M., and Zhu, J. (2018, January 18\u201323). High-resolution image synthesis and semantic manipulation with conditional gans. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, C., Zheng, H., and Yu, Z. (2018, January 8\u201314). Discriminative region proposal adversarial networks for high-quality image-to-image translation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_47"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"AlBahar, B., and Huang, J. (2019, January 15\u201320). Guided image-to-image translation with bi-directional feature transformation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00911"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3558","DOI":"10.1109\/TGRS.2019.2958123","article-title":"Triplet adversarial domain adaptation for pixel-level classification of VHR remote sensing images","volume":"58","author":"Yan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tang, H., Xu, D., and Sebe, N. (2019, January 15\u201320). Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00252"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shaham, T., Gharbi, M., and Zhang, R. (2021, January 20\u201325). Spatially-adaptive pixelwise networks for fast image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01464"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhang, B., and Chen, D. (2020, January 13\u201319). Cross-domain correspondence learning for exemplar-based image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00519"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M., and Wang, T. (2019, January 15\u201320). Semantic image synthesis with spatially-adaptive normalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"4797","article-title":"Conditional image generation with pixelcnn decoders","volume":"29","author":"Van","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","first-page":"700","article-title":"Unsupervised image-to-image translation networks","volume":"30","author":"Liu","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","first-page":"15409","article-title":"Explicitly disentangling image content from translation and rotation with spatial-VAE","volume":"32","author":"Bepler","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s11760-022-02307-y","article-title":"VAE-CoGAN: Unpaired image-to-image translation for low-level vision","volume":"17","author":"Zhang","year":"2023","journal-title":"Signal Image Video Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e190027","DOI":"10.1148\/ryai.2020190027","article-title":"Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks","volume":"2","author":"Kearney","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1090\/pcms\/025\/03","article-title":"Introductory lectures on stochastic optimization","volume":"25","author":"Duchi","year":"2018","journal-title":"Math. Data"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., and Ramos, S. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Ferrari, V. (2015, January 7\u201312). Semantic segmentation of urban scenes by learning local class interactions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1109\/TPAMI.2022.3186752","article-title":"Label-guided generative adversarial network for realistic image synthesis","volume":"45","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, S., and Wu, W. (2022, January 18\u201324). Maximum spatial perturbation consistency for unpaired image-to-image translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01777"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhou, X., and Huang, Q. (2022, January 18\u201324). Qs-attn: Query-selected attention for contrastive learning in i2i translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01775"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Torbunov, D., Huang, Y., and Yu, H. (2023, January 3\u20137). Uvcgan: Unet vision transformer cycle-consistent gan for unpaired image-to-image translation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00077"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neucom.2022.06.025","article-title":"Semantic inpainting on segmentation map via multi-expansion loss","volume":"501","author":"He","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shang, Y., Yuan, Z., and Xie, B. (2023, January 17\u201324). Post-training quantization on diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00196"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TAI.2022.3187384","article-title":"SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network","volume":"3","author":"Huang","year":"2022","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3487","DOI":"10.1109\/TIP.2021.3061286","article-title":"Complementary, heterogeneous and adversarial networks for image-to-image translation","volume":"30","author":"Gao","year":"2021","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:59Z","timestamp":1760132459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,23]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010065"],"URL":"https:\/\/doi.org\/10.3390\/rs16010065","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,23]]}}}