Feature-Guided SAR-to-Optical Image Translation
The powerful performance of Generative Adversarial Networks (GANs) in image-to-image translation has been well demonstrated in recent years. However, most methods are focused on completing an isolated image translation task. With the complex scenes in optical images and high-frequency speckle noise...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9063491/ |
| _version_ | 1818853277021765632 |
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| author | Jiexin Zhang Jianjiang Zhou Xiwen Lu |
| author_facet | Jiexin Zhang Jianjiang Zhou Xiwen Lu |
| author_sort | Jiexin Zhang |
| collection | DOAJ |
| description | The powerful performance of Generative Adversarial Networks (GANs) in image-to-image translation has been well demonstrated in recent years. However, most methods are focused on completing an isolated image translation task. With the complex scenes in optical images and high-frequency speckle noise in SAR images, the quality of generated images is often unsatisfactory. In this paper, a feature-guided method for SAR-to-optical image translation is proposed to better take the unique attributes of images into account. Specifically, in view of the diversity of structure features and texture features, VGG-19 network is used as the feature extractor in the task of cross-modal image translation. To ensure the acquisition of multilayer features in the process of image generation, feature matching is carried out on different layers. Loss function based on Discrete Cosine Transform is designed to filter out the high-frequency noise. The generated images show better performance in feature preservation and noise reduction, and achieve higher Image Quality Assessment scores compared with images generated by some famous methods. The superiority of our algorithm is also demonstrated by being applied to different networks. |
| first_indexed | 2024-12-19T07:34:15Z |
| format | Article |
| id | doaj.art-1b9e65b6f6a740bebb8490f361ac8540 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:34:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b9e65b6f6a740bebb8490f361ac85402022-12-21T20:30:37ZengIEEEIEEE Access2169-35362020-01-018709257093710.1109/ACCESS.2020.29871059063491Feature-Guided SAR-to-Optical Image TranslationJiexin Zhang0https://orcid.org/0000-0001-6945-8827Jianjiang Zhou1Xiwen Lu2Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaThe powerful performance of Generative Adversarial Networks (GANs) in image-to-image translation has been well demonstrated in recent years. However, most methods are focused on completing an isolated image translation task. With the complex scenes in optical images and high-frequency speckle noise in SAR images, the quality of generated images is often unsatisfactory. In this paper, a feature-guided method for SAR-to-optical image translation is proposed to better take the unique attributes of images into account. Specifically, in view of the diversity of structure features and texture features, VGG-19 network is used as the feature extractor in the task of cross-modal image translation. To ensure the acquisition of multilayer features in the process of image generation, feature matching is carried out on different layers. Loss function based on Discrete Cosine Transform is designed to filter out the high-frequency noise. The generated images show better performance in feature preservation and noise reduction, and achieve higher Image Quality Assessment scores compared with images generated by some famous methods. The superiority of our algorithm is also demonstrated by being applied to different networks.https://ieeexplore.ieee.org/document/9063491/SAR-to-optical image translationfeature extractionhigh-frequency noisegenerative adversarial networks |
| spellingShingle | Jiexin Zhang Jianjiang Zhou Xiwen Lu Feature-Guided SAR-to-Optical Image Translation IEEE Access SAR-to-optical image translation feature extraction high-frequency noise generative adversarial networks |
| title | Feature-Guided SAR-to-Optical Image Translation |
| title_full | Feature-Guided SAR-to-Optical Image Translation |
| title_fullStr | Feature-Guided SAR-to-Optical Image Translation |
| title_full_unstemmed | Feature-Guided SAR-to-Optical Image Translation |
| title_short | Feature-Guided SAR-to-Optical Image Translation |
| title_sort | feature guided sar to optical image translation |
| topic | SAR-to-optical image translation feature extraction high-frequency noise generative adversarial networks |
| url | https://ieeexplore.ieee.org/document/9063491/ |
| work_keys_str_mv | AT jiexinzhang featureguidedsartoopticalimagetranslation AT jianjiangzhou featureguidedsartoopticalimagetranslation AT xiwenlu featureguidedsartoopticalimagetranslation |