A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism
High-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolution...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/13/2995 |
| _version_ | 1797591819342577664 |
|---|---|
| author | Yan Shi Chun Jiang Changhua Liu Wenhan Li Zhiyong Wu |
| author_facet | Yan Shi Chun Jiang Changhua Liu Wenhan Li Zhiyong Wu |
| author_sort | Yan Shi |
| collection | DOAJ |
| description | High-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolutional attention mechanism (DCAM). Firstly, the mapping space is constrained by dual regression; secondly, deformable convolution is used to expand the perceptual field and extract the high-frequency features of the image; finally, the convolutional attention mechanism is used to calculate the saliency of the channel domain and the spatial domain of the image to enhance the useful features and suppress the useless feature responses. The experimental results show that the method outperforms the comparison algorithm in both objective quality evaluation index and localization accuracy on the space target image dataset compared with the current mainstream image super-resolution algorithms. |
| first_indexed | 2024-03-11T01:42:47Z |
| format | Article |
| id | doaj.art-32cbc344acfe428db7568c54cea32e2d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:42:47Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-32cbc344acfe428db7568c54cea32e2d2023-11-18T16:26:29ZengMDPI AGElectronics2079-92922023-07-011213299510.3390/electronics12132995A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention MechanismYan Shi0Chun Jiang1Changhua Liu2Wenhan Li3Zhiyong Wu4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaHigh-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolutional attention mechanism (DCAM). Firstly, the mapping space is constrained by dual regression; secondly, deformable convolution is used to expand the perceptual field and extract the high-frequency features of the image; finally, the convolutional attention mechanism is used to calculate the saliency of the channel domain and the spatial domain of the image to enhance the useful features and suppress the useless feature responses. The experimental results show that the method outperforms the comparison algorithm in both objective quality evaluation index and localization accuracy on the space target image dataset compared with the current mainstream image super-resolution algorithms.https://www.mdpi.com/2079-9292/12/13/2995super-resolution reconstructionspace targetdual regressiondeformable convolutional attention mechanism |
| spellingShingle | Yan Shi Chun Jiang Changhua Liu Wenhan Li Zhiyong Wu A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism Electronics super-resolution reconstruction space target dual regression deformable convolutional attention mechanism |
| title | A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism |
| title_full | A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism |
| title_fullStr | A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism |
| title_full_unstemmed | A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism |
| title_short | A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism |
| title_sort | super resolution reconstruction network of space target images based on dual regression and deformable convolutional attention mechanism |
| topic | super-resolution reconstruction space target dual regression deformable convolutional attention mechanism |
| url | https://www.mdpi.com/2079-9292/12/13/2995 |
| work_keys_str_mv | AT yanshi asuperresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT chunjiang asuperresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT changhualiu asuperresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT wenhanli asuperresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT zhiyongwu asuperresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT yanshi superresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT chunjiang superresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT changhualiu superresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT wenhanli superresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism AT zhiyongwu superresolutionreconstructionnetworkofspacetargetimagesbasedondualregressionanddeformableconvolutionalattentionmechanism |