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Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images.
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1985
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1990
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1995
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R. Touzi, “A review of speckle filtering in the context of estimation theory,” IEEE Transactions on Geoscience and Remote Sensing , vol. 40, no. 11, pp. 2392–2404, 2002
2002
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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Processing , vol. 16, no. 8, pp. 2080–2095, 2007
2007
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C.-A. Deledalle, L. Denis, and F. Tupin, “Iterative weighted maximum likelihood denoising with probabilistic patch-based weights,” IEEE Transactions on Image Processing , vol. 18, no. 12, pp. 2661–2672, 2009
2009
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S. Parrilli, M. Poderico, C. V. Angelino, and L. Verdoliva, “A nonlocal sar image denoising algorithm based on llmmse wavelet shrinkage,” IEEE Transactions on Geoscience and Remote Sensing , vol. 50, no. 2, pp. 606–616, 2012
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2013
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V. M. Patel, G. R. Easley, R. Chellappa, and N. M. Nasrabadi, “Separated component-based restoration of speckled sar images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 52, no. 2, pp. 1019–1029, 2014
2014
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C.-A. Deledalle, L. Denis, G. Poggi, F. Tupin, and L. Verdoliva, “Exploiting patch similarity for sar image processing: The nonlocal paradigm,” IEEE Signal Processing Magazine , vol. 31, no. 4, pp. 69–78, 2014
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds., vol. 27. Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
2014
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI , 2015
2015
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G. Chierchia, D. Cozzolino, G. Poggi, and L. Verdoliva, “Sar image despeckling through convolutional neural networks,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , 2017, pp. 5438–5441
C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu, “A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 166, pp. 183–200, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0924271620301532
2020
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S. Vitale, G. Ferraioli, and V. Pascazio, “Multi-objective cnn-based algorithm for sar despeckling,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 11, pp. 9336–9349, 2021
2021
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S. Liu, Y. Lei, L. Zhang, B. Li, W. Hu, and Y.-D. Zhang, “Mrddanet: A multiscale residual dense dual attention network for sar image denoising,” IEEE Transactions on Geoscience and Remote Sensing , pp. 1–13, 2021
2021
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2017
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P. Wang, H. Zhang, and V. M. Patel, “Sar image despeckling using a convolutional neural network,” IEEE Signal Processing Letters , vol. 24, no. 12, pp. 1763–1767, 2017
2017
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——, “Generative adversarial network-based restoration of speckled sar images,” in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) , 2017, pp. 1–5
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
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M. Schmitt, L. Hughes, and X. Zhu, “The sen1-2 dataset for deep learning in sar-optical data fusion,” in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. IV-1, 09 2018, pp. 141–146
2018
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A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=B1xsqj09Fm
2019
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 6840–6851. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf
2020
Cited alongside, same era.
2021
Later among the works it cites.
J. Ko and S. Lee, “Sar image despeckling using continuous attention module,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 3–19, 2022
2022
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H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen, “Srdiff: Single image super-resolution with diffusion probabilistic models,” Neurocomputing , vol. 479, pp. 47–59, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231222000522
2022
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