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Pan-sharpening aims at producing a high-resolution (HR) multi-spectral (MS) image from a low-resolution (LR) multi-spectral (MS) image and its corresponding panchromatic (PAN) image acquired by a same satellite.
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J. Zhou, D. L. Civco, and J. A. Silander, · 1998
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“Quality of high resolution synthesised images: Is there a simple criterion?,”
L. Wald, · 2000
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“Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening,” Jan. 4 2000,
C. A Laben and B. V Brower, · 2000
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Z. Wang, A. C. Bovik, H. R. Sheikh, et al., · 2004
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“Optimal mmse pan sharpening of very high resolution multispectral images,”
A. Garzelli, F. Nencini, and L. Capobianco, · 2008
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“High-fidelity component substitution pansharpening by the fitting of substitution data,”
Q. Xu, B. Li, Y. Zhang, et al., · 2014
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“Generative adversarial networks,”
I. J Goodfellow, J. Pouget-Abadie, M. Mirza, et al., · 2014
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“Adam: A method for stochastic optimization,”
D. P Kingma and J. Ba, · 2014
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“Pansharpening by convolutional neural networks,”
G. Masi, D. Cozzolino, L. Verdoliva, et al., · 2016
Cited alongside, same era.
“Image super-resolution using deep convolutional networks,”
C. Dong, C. C. Loy, K. He, et al., · 2016
Cited alongside, same era.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, et al., · 2016
Cited alongside, same era.
“Pannet: A deep network architecture for pan-sharpening,”
J. Yang, X. Fu, Y. Hu, et al., · 2017
Cited alongside, same era.
“Boosting the accuracy of multispectral image pansharpening by learning a deep residual network,”
Y. Wei, Q. Yuan, H. Shen, et al., · 2017
Cited alongside, same era.
“Attention is all you need,”
A. Vaswani, N. Shazeer, N. Parmar, et al., · 2017
Cited alongside, same era.
“MGHCNET: A deep multi-scale granular and holistic channel feature generation network for image super resolution,”
A. Esmaeilzehi, M. O. Ahmad, and M. N. S. Swamy, · 2020
Later among the works it cites.
“Dsr: An accurate single image super resolution approach for various degradations,”
Y. Mei, Y. Zhao, and W. Liang, · 2020
Later among the works it cites.
“Pan-gan: An unsupervised pan-sharpening method for remote sensing image fusion,”
J. Ma, W. Yu, C. Chen, et al., · 2020
Later among the works it cites.
“Remote sensing image fusion based on two-stream fusion network,”
X. Liu, Q. Liu, and Y. Wang, · 2020
Later among the works it cites.
“Psgan: A generative adversarial network for remote sensing image pan-sharpening,”
Q. Liu, H. Zhou, Q. Xu, et al., · 2020
Later among the works it cites.
“Detail injection-based deep convolutional neural networks for pansharpening,”
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“A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening,”
Q. Yuan, Y. Wei, X. Meng, et al., · 2018
Cited alongside, same era.
“Image transformer,”
N. Parmar, A. Vaswani, J. Uszkoreit, et al., · 2018
Cited alongside, same era.
“Pytorch: An imperative style, high-performance deep learning library,”
A. Paszke, S. Gross, F. Massa, et al., · 2019
Cited alongside, same era.
L. J. Deng, G. Vivone, C. Jin, et al., · 2021
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“An image is worth 16x16 words: Transformers for image recognition at scale,”
A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., · 2021
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“Swin transformer: Hierarchical vision transformer using shifted windows,”
Z. Liu, Y. Lin, Y. Cao, et al., · 2021
Later among the works it cites.
“Deep gradient projection networks for pan-sharpening,”
S. Xu, J. Zhang, Z. Zhao, et al., · 2021
Later among the works it cites.