Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing
Abstract The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and ph...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83088-x |
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| author | Yanfei Jia Wenshuo Yu Liquan Zhao |
| author_facet | Yanfei Jia Wenshuo Yu Liquan Zhao |
| author_sort | Yanfei Jia |
| collection | DOAJ |
| description | Abstract The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image. A multi-scale module is proposed to extract feature information of remote sensing images, allowing it to capture image features from different receptive fields. Simultaneously, an attention module is designed further to guide the network’s focus towards important feature information. In addition, a multi-scale adversarial network is proposed to better restore both global and local information about the original image. Introducing a physical constraint loss function to improve the loss function of the original generative adversarial network allows for better preservation of the physical characteristics of remote sensing images. Simulation experiments on synthetic and natural hazy remote sensing image datasets are conducted. The results demonstrate that the dehazing performance of the TRPC-GAN method surpasses the other four methods. |
| format | Article |
| id | doaj-art-c25d01659ead41debc45a9eb465df0e0 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c25d01659ead41debc45a9eb465df0e02025-08-20T02:57:32ZengNature PortfolioScientific Reports2045-23222024-12-0114111610.1038/s41598-024-83088-xGenerative adversarial networks with texture recovery and physical constraints for remote sensing image dehazingYanfei Jia0Wenshuo Yu1Liquan Zhao2College of Electrical and Electronic Information Engineering, Beihua UniversityDepartment of Instrument Science and Electrical Engineering, Jilin UniversityCollege of Electrical Engineering, Northeast Electric Power UniversityAbstract The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image. A multi-scale module is proposed to extract feature information of remote sensing images, allowing it to capture image features from different receptive fields. Simultaneously, an attention module is designed further to guide the network’s focus towards important feature information. In addition, a multi-scale adversarial network is proposed to better restore both global and local information about the original image. Introducing a physical constraint loss function to improve the loss function of the original generative adversarial network allows for better preservation of the physical characteristics of remote sensing images. Simulation experiments on synthetic and natural hazy remote sensing image datasets are conducted. The results demonstrate that the dehazing performance of the TRPC-GAN method surpasses the other four methods.https://doi.org/10.1038/s41598-024-83088-xRemote sensingDeep learningGenerative adversarial networksTexture recovery and physical constraints |
| spellingShingle | Yanfei Jia Wenshuo Yu Liquan Zhao Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing Scientific Reports Remote sensing Deep learning Generative adversarial networks Texture recovery and physical constraints |
| title | Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| title_full | Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| title_fullStr | Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| title_full_unstemmed | Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| title_short | Generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| title_sort | generative adversarial networks with texture recovery and physical constraints for remote sensing image dehazing |
| topic | Remote sensing Deep learning Generative adversarial networks Texture recovery and physical constraints |
| url | https://doi.org/10.1038/s41598-024-83088-x |
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