{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:18:46Z","timestamp":1783664326356,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T00:00:00Z","timestamp":1734825600000},"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":["62401012"],"award-info":[{"award-number":["62401012"]}],"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":["QZ202313"],"award-info":[{"award-number":["QZ202313"]}],"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":["GXXT-2023-021"],"award-info":[{"award-number":["GXXT-2023-021"]}],"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":["2022AH050319"],"award-info":[{"award-number":["2022AH050319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui University of Technology Young Teachers Research Fund Project","award":["62401012"],"award-info":[{"award-number":["62401012"]}]},{"name":"Anhui University of Technology Young Teachers Research Fund Project","award":["QZ202313"],"award-info":[{"award-number":["QZ202313"]}]},{"name":"Anhui University of Technology Young Teachers Research Fund Project","award":["GXXT-2023-021"],"award-info":[{"award-number":["GXXT-2023-021"]}]},{"name":"Anhui University of Technology Young Teachers Research Fund Project","award":["2022AH050319"],"award-info":[{"award-number":["2022AH050319"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["62401012"],"award-info":[{"award-number":["62401012"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["QZ202313"],"award-info":[{"award-number":["QZ202313"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["GXXT-2023-021"],"award-info":[{"award-number":["GXXT-2023-021"]}]},{"name":"University Synergy Innovation Program of Anhui Province","award":["2022AH050319"],"award-info":[{"award-number":["2022AH050319"]}]},{"name":"Key Program of the Natural Science Foundation of the Educational Department of Anhui Province of China","award":["62401012"],"award-info":[{"award-number":["62401012"]}]},{"name":"Key Program of the Natural Science Foundation of the Educational Department of Anhui Province of China","award":["QZ202313"],"award-info":[{"award-number":["QZ202313"]}]},{"name":"Key Program of the Natural Science Foundation of the Educational Department of Anhui Province of China","award":["GXXT-2023-021"],"award-info":[{"award-number":["GXXT-2023-021"]}]},{"name":"Key Program of the Natural Science Foundation of the Educational Department of Anhui Province of China","award":["2022AH050319"],"award-info":[{"award-number":["2022AH050319"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques.<\/jats:p>","DOI":"10.3390\/rs16244780","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:13:38Z","timestamp":1734945218000},"page":"4780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-Dimensional and Multi-Scale Physical Dehazing Network for Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-6784","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Ma\u2019anshan 243002, China"},{"name":"School of Big Data and Intelligent Engineering, Chongqing College of International Business and Economics, Chongqing 401520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5710-2071","authenticated-orcid":false,"given":"Le","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Ma\u2019anshan 243002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5630-2070","authenticated-orcid":false,"given":"Tao","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, Ma\u2019anshan 243002, China"},{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kulk, G., Platt, T., Dingle, J., Jackson, T., J\u00f6nsson, B.F., Bouman, H.A., Babin, M., Brewin, R.J., Doblin, M., and Estrada, M. (2020). Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens., 12.","DOI":"10.3390\/rs12050826"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103027","DOI":"10.1016\/j.agsy.2020.103027","article-title":"Multi-level impacts of the COVID-19 lockdown on agricultural systems in India: The case of Uttar Pradesh","volume":"187","author":"Kumar","year":"2021","journal-title":"Agric. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"05021029","DOI":"10.1061\/(ASCE)UP.1943-5444.0000712","article-title":"Relationship between blue economy, cruise tourism, and urban regeneration: Case study of Olbia, Sardinia","volume":"147","year":"2021","journal-title":"J. Urban Plan. Dev."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, S., Fang, H., and Zhang, Y. (2023). Determination of the leaf inclination angle (LIA) through field and remote sensing methods: Current status and future prospects. Remote Sens., 15.","DOI":"10.3390\/rs15040946"},{"key":"ref_5","unstructured":"McCartney, E. (1976). Optics of the Atmosphere: Scattering by Molecules and Particles, John Wiley and Sons, Inc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/ICCV.1999.790306","article-title":"Vision in bad weather","volume":"Volume 2","author":"Nayar","year":"1999","journal-title":"Proceedings of the seventh IEEE international Conference on Computer Vision"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Phutke, S.S., Vipparthi, S.K., and Murala, S. (2024, January 4\u20138). C2AIR: Consolidated Compact Aerial Image Haze Removal. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00080"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110370","DOI":"10.1016\/j.patcog.2024.110370","article-title":"Wavelet-based Auto-Encoder for simultaneous haze and rain removal from images","volume":"150","author":"Ali","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109956","DOI":"10.1016\/j.patcog.2023.109956","article-title":"Restoring vision in hazy weather with hierarchical contrastive learning","volume":"145","author":"Wang","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_10","unstructured":"Liu, Y., Yan, Z., Chen, S., Ye, T., Ren, W., and Chen, E. (November, January 29). Nighthazeformer: Single nighttime haze removal using prior query transformer. Proceedings of the Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TIP.2018.2883741","article-title":"Two-Stream Convolutional Networks for Blind Image Quality Assessment","volume":"28","author":"Yan","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.inffus.2012.05.002","article-title":"Multi-metric learning for multi-sensor fusion based classification","volume":"14","author":"Zhang","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"Dehazenet: An end-to-end system for single image haze removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1109\/TCSVT.2018.2880223","article-title":"Visual haze removal by a unified generative adversarial network","volume":"29","author":"Pang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., and Yang, M.H. (2020, January 11\u201315). Multi-scale boosted dehazing network with dense feature fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, X., Ma, Y., Shi, Z., and Chen, J. (2019, January 11\u201317). Griddehazenet: Attention-based multi-scale network for image dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV.2019.00741"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, January 7\u201312). FFA-Net: Feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., and Li, H. (2022, January 18\u201324). Uformer: A general u-shaped transformer for image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","article-title":"Vision transformers for single image dehazing","volume":"32","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nie, J., Xie, J., and Sun, H. (2024). Remote Sensing Image Dehazing via a Local Context-Enriched Transformer. Remote Sens., 16.","DOI":"10.3390\/rs16081422"},{"key":"ref_21","unstructured":"Shi, Y., Xia, B., Jin, X., Wang, X., Zhao, T., Xia, X., Xiao, X., and Yang, W. (2024). Vmambair: Visual state space model for image restoration. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106281","DOI":"10.1016\/j.neunet.2024.106281","article-title":"Frequency compensated diffusion model for real-scene dehazing","volume":"175","author":"Wang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8001805","DOI":"10.1109\/LGRS.2023.3309655","article-title":"Remote sensing image dehazing using adaptive region-based diffusion models","volume":"20","author":"Huang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110410","DOI":"10.1016\/j.knosys.2023.110410","article-title":"Physical-priors-guided DehazeFormer","volume":"266","author":"Zhou","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_25","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Part III 18."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, B., Zhi, L., Yang, C., Sun, F., and Song, Y. (2020, January 1\u20135). Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093471"},{"key":"ref_27","first-page":"1","article-title":"Dense haze removal based on dynamic collaborative inference learning for remote sensing images","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tan, R.T. (2008, January 23\u201328). Visibility in bad weather from a single image. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587643"},{"key":"ref_29","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2651362","article-title":"Dehazing using color-lines","volume":"34","author":"Fattal","year":"2014","journal-title":"Acm Trans. Graph. (Tog)"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, K., Yang, J., and Wang, J. (2014, January 23\u201328). Investigating haze-relevant features in a learning framework for image dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.383"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","article-title":"A fast single image haze removal algorithm using color attenuation prior","volume":"24","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Berman, D., and Avidan, S. (2016, January 27\u201330). Non-local image dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.185"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017, January 22\u201329). Aod-net: All-in-one dehazing network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_35","unstructured":"Mei, K., Jiang, A., Li, J., and Wang, M. (2018, January 2\u20136). Progressive feature fusion network for realistic image dehazing. Proceedings of the Computer Vision\u2013ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia. Revised Selected Papers, Part I 14."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lu, L., Xiong, Q., Xu, B., and Chu, D. (2024, January 30). Mixdehazenet: Mix structure block for image dehazing network. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan.","DOI":"10.1109\/IJCNN60899.2024.10651326"},{"key":"ref_37","unstructured":"Cui, Y., Ren, W., and Knoll, A. (2024, January 20\u201327). Omni-Kernel Network for Image Restoration. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4106516","DOI":"10.1109\/TGRS.2023.3321307","article-title":"Partial siamese with multiscale bi-codec networks for remote sensing image haze removal","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8002305","DOI":"10.1109\/LGRS.2023.3319832","article-title":"Learning an effective transformer for remote sensing satellite image dehazing","volume":"20","author":"Song","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, Y., Li, C., Li, X., and Bai, T. (2024). A Lightweight CNN Based on Axial Depthwise Convolution and Hybrid Attention for Remote Sensing Image Dehazing. Remote Sens., 16.","DOI":"10.3390\/rs16152822"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.M. (2018, January 18\u201323). Densely connected pyramid dehazing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00337"},{"key":"ref_42","unstructured":"Liu, Y., Pan, J., Ren, J., and Su, Z. (November, January 27). Learning deep priors for image dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1109\/TIP.2021.3060873","article-title":"RefineDNet: A weakly supervised refinement framework for single image dehazing","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhan, J., He, S., Dong, J., and Du, Y. (2023, January 17\u201324). Curricular contrastive regularization for physics-aware single image dehazing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00560"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108548","DOI":"10.1016\/j.patcog.2022.108548","article-title":"mSODANet: A network for multi-scale object detection in aerial images using hierarchical dilated convolutions","volume":"126","author":"Chalavadi","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, X., Bai, Y., Wang, Y., and Fu, Y. (2024, January 17\u201321). Rewrite the Stars. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00544"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201319). Selective kernel networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1109\/TPAMI.2022.3167175","article-title":"Learning enriched features for fast image restoration and enhancement","volume":"45","author":"Zamir","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","first-page":"1001711","article-title":"An Oriented Object Detector for Hazy Remote Sensing Images","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1109\/LGRS.2020.3006533","article-title":"A coarse-to-fine two-stage attentive network for haze removal of remote sensing images","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote. Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:47Z","timestamp":1760115467000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,22]]},"references-count":50,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244780"],"URL":"https:\/\/doi.org\/10.3390\/rs16244780","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,22]]}}}