| 计算机技术、信息工程 |
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| 基于CNN和Transformer聚合的遥感图像超分辨率重建 |
胡明志( ),孙俊*(),杨彪,常开荣,杨俊龙 |
| 昆明理工大学 信息工程与自动化学院，云南 昆明 650500 |
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| Super-resolution reconstruction of remote sensing image based on CNN and Transformer aggregation |
| Mingzhi HU(),Jun SUN*(),Biao YANG,Kairong CHANG,Junlong YANG |
| School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China |
引用本文:
胡明志,孙俊,杨彪,常开荣,杨俊龙. 基于CNN和Transformer聚合的遥感图像超分辨率重建[J]. 浙江大学学报(工学版), 2025, 59(5): 938-946.
Mingzhi HU,Jun SUN,Biao YANG,Kairong CHANG,Junlong YANG. Super-resolution reconstruction of remote sensing image based on CNN and Transformer aggregation. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 938-946.
链接本文:
https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.05.007
或
https://www.zjujournals.com/eng/CN/Y2025/V59/I5/938
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| 1 |
ZHANG H, YANG Z, ZHANG L, et al Super-resolution reconstruction for multi-angle remote sensing images considering resolution differences[J]. Remote Sensing, 2014, 6 (1): 637- 657
doi: 10.3390/rs6010637
|
| 2 |
PAPATHANASSIOU C, PETROU M. Super resolution: an overview [C]// IEEE International Geoscience and Remote Sensing Symposium . Seoul: IEEE, 2005: 5655-5658.
|
| 3 |
GLASNER D, BAGON S, IRANI M. Super-resolution from a single image [C]// IEEE 12th International Conference on Computer Vision . Kyoto: IEEE, 2009: 349-356.
|
| 4 |
DONG C, LOY C C, HE K, et al Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38 (2): 295- 307
|
| 5 |
LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . Honolulu: IEEE, 2017: 136-144.
|
| 6 |
BEGIN I, FERRIE F R. Blind super-resolution using a learning-based approach [C]// Proceedings of the 17th International Conference on Pattern Recognition . Cambridge: IEEE, 2004: 85-89.
|
| 7 |
JOSHI M V, CHAUDHURI S, PANUGANTI R A learning-based method for image super-resolution from zoomed observations[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35 (3): 527- 537
doi: 10.1109/TSMCB.2005.846647
|
| 8 |
CHAN T M, ZHANG J. An improved super-resolution with manifold learning and histogram matching [C]// Advances in Biometrics: International Conference . Hong Kong: Springer, 2005: 756-762.
|
| 9 |
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale [C]// International Conference on Learning Representations . Ethiopia: [s. n.], 2020.
|
| 10 |
DONG C, LOY C C, HE K, et al. Learning a deep convolutional network for image super-resolution [C]// 13th European Conference on Computer Vision . Switzerland: Springer, 2014: 184-199.
|
| 11 |
KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 1646-1654.
|
| 12 |
LI W, ZHOU K, QI L, et al Lapar: linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond[J]. Advances in Neural Information Processing Systems, 2020, 33: 20343- 20355
|
| 13 |
LIANG J, CAO J, SUN G, et al. Swinir: image restoration using swin transformer [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Montreal: IEEE, 2021: 1833-1844.
|
| 14 |
CHEN H, WANG Y, GUO T, et al. Pre-trained image processing transformer [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville: IEEE, 2021: 12299-12310.
|
| 15 |
LEI S, SHI Z, ZOU Z Super-resolution for remote sensing images via local–global combined network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (8): 1243- 1247
doi: 10.1109/LGRS.2017.2704122
|
| 16 |
PAN Z, MA W, GUO J, et al Super-resolution of single remote sensing image based on residual dense backprojection networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57 (10): 7918- 7933
doi: 10.1109/TGRS.2019.2917427
|
| 17 |
ZHANG D, SHAO J, LI X, et al Remote sensing image super-resolution via mixed high-order attention network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59 (6): 5183- 5196
|
| 18 |
BAI J, YUAN L, XIA S T, et al. Improving vision transformers by revisiting high-frequency components [C]// European Conference on Computer Vision . Cham: Springer, 2022: 1-18.
|
| 19 |
ELAD M, FEUER A Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images[J]. IEEE Transactions on Image Processing, 1997, 6 (12): 1646- 1658
doi: 10.1109/83.650118
|
| 20 |
LIU C, SUN D On Bayesian adaptive video super resolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 36 (2): 346- 360
|
| 21 |
ZHANG K, LIANG J, VAN GOOL L, et al. Designing a practical degradation model for deep blind image super-resolution [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Montreal: IEEE, 2021: 4791-4800.
|
| 22 |
LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Montreal: IEEE, 2021: 10012-10022.
|
| 23 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [EB/OL]. [2024-05-15]. https://arxiv.org/abs/1706.03762.
|
| 24 |
ZAMIR S W, ARORA A, KHAN S, et al. Restormer: efficient transformer for high-resolution image restoration [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans: IEEE, 2022: 5728-5739.
|
| 25 |
XIA G, HU J, HU F, et al AID: a benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55 (7): 3965- 3981
doi: 10.1109/TGRS.2017.2685945
|
| 26 |
DAI D, YANG W Satellite image classification via two-layer sparse coding with biased image representation[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 8 (1): 173- 176
|
| 27 |
TANCHENKO A Visual-PSNR measure of image quality[J]. Journal of Visual Communication and Image Representation, 2014, 25 (5): 874- 878
doi: 10.1016/j.jvcir.2014.01.008
|
| 28 |
WANG Z, BOVIK A C, SHEIKH H R, et al Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13 (4): 600- 612
doi: 10.1109/TIP.2003.819861
|
| 29 |
ZHANG W, LI X, SHI G, et al. Real-world image super-resolution as multi-task learning [J]. Advances in Neural Information Processing Systems , 2023, 36: 21003-21022.
|
| 30 |
WANG X, XIE Liangbin, DONG C, et al. Real-esrgan: training real-world blind super-resolution with pure synthetic data [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Montreal: IEEE, 2021: 1905-1914.
|
| 31 |
MOU C, WU Y, WANG X, et al. Metric learning based interactive modulation for real-world super-resolution [C]// European Conference on Computer Vision . Cham: Springer, 2022: 723-740.
|
| 32 |
WEI P, XIE Z, LU H, et al. Component divide-and-conquer for real-world image super-resolution [C]// 16th European Conference on Computer Vision . Glasgow: Springer, 2020: 101-117.
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