DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution
The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing task...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9976189/ |
| _version_ | 1797975350916939776 |
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| author | Qingjian Wang Sen Wang Mingfang Chen Yang Zhu |
| author_facet | Qingjian Wang Sen Wang Mingfang Chen Yang Zhu |
| author_sort | Qingjian Wang |
| collection | DOAJ |
| description | The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing tasks. In this article, we propose a compact and efficient distance attention residual network (DARN) to achieve a better compromise between model accuracy and complexity. The distance attention residual connection block (DARCB), the core component of the DARN, uses multistage feature aggregation to learn more accurate feature representations. The main branch of the DARCB adopts a shallow residual block (SRB) to flexibly learn residual information to ensure the robustness of the model. We also propose a distance attention block (DAB) as a bridge between the main branch and the branch of the DARCB; the DAB can effectively alleviate the loss of detail features in the deep CNN extraction process. Experimental results on two remote sensing and five super-resolution benchmark datasets demonstrate that the DARN achieves a better compromise than existing methods in terms of performance and model complexity. In addition, the DARN achieves the optimal solution compared with the state-of-the-art lightweight remote sensing SISR method in terms of parameter amount, computation amount, and inference speed. Our code will be available at <uri>https://github.com/candygogogogo/DARN</uri>. |
| first_indexed | 2024-04-11T04:34:09Z |
| format | Article |
| id | doaj.art-eb0c3eb721b643bc8367590beb1aefb9 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T04:34:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-eb0c3eb721b643bc8367590beb1aefb92022-12-29T00:00:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-011671472410.1109/JSTARS.2022.32275099976189DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image SuperresolutionQingjian Wang0https://orcid.org/0000-0002-8132-5899Sen Wang1https://orcid.org/0000-0003-1259-8030Mingfang Chen2https://orcid.org/0000-0002-3323-8168Yang Zhu3https://orcid.org/0000-0001-5493-3250Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaThe application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing tasks. In this article, we propose a compact and efficient distance attention residual network (DARN) to achieve a better compromise between model accuracy and complexity. The distance attention residual connection block (DARCB), the core component of the DARN, uses multistage feature aggregation to learn more accurate feature representations. The main branch of the DARCB adopts a shallow residual block (SRB) to flexibly learn residual information to ensure the robustness of the model. We also propose a distance attention block (DAB) as a bridge between the main branch and the branch of the DARCB; the DAB can effectively alleviate the loss of detail features in the deep CNN extraction process. Experimental results on two remote sensing and five super-resolution benchmark datasets demonstrate that the DARN achieves a better compromise than existing methods in terms of performance and model complexity. In addition, the DARN achieves the optimal solution compared with the state-of-the-art lightweight remote sensing SISR method in terms of parameter amount, computation amount, and inference speed. Our code will be available at <uri>https://github.com/candygogogogo/DARN</uri>.https://ieeexplore.ieee.org/document/9976189/Convolution neural networklightweightremote sensingsingle image superresolution (SISR) |
| spellingShingle | Qingjian Wang Sen Wang Mingfang Chen Yang Zhu DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolution neural network lightweight remote sensing single image superresolution (SISR) |
| title | DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution |
| title_full | DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution |
| title_fullStr | DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution |
| title_full_unstemmed | DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution |
| title_short | DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution |
| title_sort | darn distance attention residual network for lightweight remote sensing image superresolution |
| topic | Convolution neural network lightweight remote sensing single image superresolution (SISR) |
| url | https://ieeexplore.ieee.org/document/9976189/ |
| work_keys_str_mv | AT qingjianwang darndistanceattentionresidualnetworkforlightweightremotesensingimagesuperresolution AT senwang darndistanceattentionresidualnetworkforlightweightremotesensingimagesuperresolution AT mingfangchen darndistanceattentionresidualnetworkforlightweightremotesensingimagesuperresolution AT yangzhu darndistanceattentionresidualnetworkforlightweightremotesensingimagesuperresolution |