IEEE Geoscience and Remote Sensing Magazine - June 2022 - 226

[25] J. M. Bioucas-Dias and M. A. Figueiredo, " Multiplicative noise
removal using variable splitting and constrained optimization, "
IEEE Trans. Image Process., vol. 19, no. 7, pp. 1720-1730,
2010, doi: 10.1109/TIP.2010.2045029.
[26] L Denis, F Tupin, and X Rondeau, " Exact discrete minimization
for TV+L0 image decomposition models, " in Proc.
Int. Conf. Image Process., 2010, pp. 2525-2528, doi: 10.1109/
ICIP.2010.5649204.
[27] S. Lobry, L. Denis, and F. Tupin, " Multitemporal SAR image decomposition
into strong scatterers, background, and speckle, "
IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 8,
pp. 3419-3429, 2016, doi: 10.1109/JSTARS.2016.2555579.
[28] H. Xie, L. E. Pierce, and F. T. Ulaby, " SAR speckle reduction using
wavelet denoising and Markov random field modeling, "
IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp. 2196-2212,
2002, doi: 10.1109/TGRS.2002.802473.
[29] S. Durand, J. Fadili, and M. Nikolova, " Multiplicative noise cleaning
via a variational method involving curvelet coefficients, " in
Proc. Int. Conf. Scale Space Variational Methods Comput. Vis., SpringerVerlag,
2009, pp. 282-294, doi: 10.1007/978-3-642-02256-2_24.
[30] G. Ferraioli, C. Deledalle, L. Denis, and F. Tupin, " PARISAR:
Patch-based estimation and regularized inversion for multibaseline
SAR interferometry, " IEEE Trans. Geosci. Remote Sens., vol. 56,
no. 3, pp. 1626-1636, 2017, doi: 10.1109/TGRS.2017.2765761.
[31] C.-A. Deledalle, L. Denis, S. Tabti, and F. Tupin, " MuLoG, or
how to apply Gaussian denoisers to multi-channel SAR speckle
reduction? " IEEE Trans. Image Process., vol. 26, no. 9, pp. 4389-
4403, 2017, doi: 10.1109/TIP.2017.2713946.
[32] X. Yang, L. Denis, F. Tupin, and W. Yang, " SAR image despeckling
using pre-trained convolutional neural network models, "
in Proc. Joint Urban Remote Sensing Event, 2019, pp. 1-4, doi:
10.1109/JURSE.2019.8809023.
[33] E. Dalsasso, X. Yang, L. Denis, F. Tupin, and W. Yang, " SAR Image
Despeckling by Deep Neural Networks: From a pre-trained
model to an end-to-end training strategy, " Remote Sens., vol. 12,
no. 16, p. 2636, 2020, doi: 10.3390/rs12162636.
[34] G. Chierchia, D. Cozzolino, G. Poggi, and L. Verdoliva, " SAR
image despeckling through convolutional neural networks, " in
Proc. IEEE Int. Geosci. Remote Sens. Symp., 2017, pp. 5438-5441,
doi: 10.1109/IGARSS.2017.8128234.
[35] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, " Beyond
a gaussian denoiser: Residual learning of deep CNN for image
denoising, " IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142-
3155, 2017, doi: 10.1109/TIP.2017.2662206.
[36] D. Cozzolino, L. Verdoliva, G. Scarpa, and G. Poggi, " Nonlocal
CNN SAR Image Despeckling, " Remote Sens., vol. 12, no. 6, p.
1006, 2020, doi: 10.3390/rs12061006.
[37] T. Plötz and S. Roth, " Neural nearest neighbors networks, " in
Proc. Adv. Neural Inform. Process. Syst., 2018, pp. 1087-1098.
[38] F. Lattari, B. Gonzalez Leon, F. Asaro, A. Rucci, C. Prati, and M.
Matteucci, " Deep learning for SAR image despeckling, " Remote
Sens., vol. 11, no. 13, p. 1532, 2019, doi: 10.3390/rs11131532.
[39] P. Wang, H. Zhang, and V. M. Patel, " SAR image despeckling
using a convolutional neural network, " IEEE Signal Process.
Lett., vol. 24, no. 12, pp. 1763-1767, 2017, doi: 10.1109/
LSP.2017.2758203.
226
IEEE
[40] P. Wang, H. Zhang, and V. M Patel, " Generative adversarial network-based
restoration of speckled SAR images, " in Proc. 2017
IEEE 7th Int. Workshop Comput. Adv. Multi-Sens. Adaptive Process.,
pp. 1-5, doi: 10.1109/CAMSAP.2017.8313133.
[41] Q. Zhang, Q. Yuan, J. Li, Z. Yang, and X. Ma, " Learning a dilated
residual network for SAR image despeckling, " Remote Sens.,
vol. 10, no. 2, p. 196, 2018, doi: 10.3390/rs10020196.
[42] E. Dalsasso, L. Denis, and F. Tupin, " How to handle spatial correlations
in SAR despeckling? Resampling strategies and deep
learning approaches, " in Proc. 13th Eur. Conf. Synthetic Aperture
Radar, 2020, pp. 1-6.
[43] S. Vitale, G. Ferraioli, and V. Pascazio, " A new ratio image
based CNN algorithm FOR SAR despeckling, " in Proc. IEEE Int.
Geosci. Remote Sens. Symp., 2019, pp. 9494-9497, doi: 10.1109/
IGARSS.2019.8899245.
[44] S. Vitale, G. Ferraioli, and V. Pascazio, " Edge preserving CNN
SAR despeckling algorithm, " in Proc. IEEE Latin Amer. GRSS
ISPRS Remote Sens. Conf., 2020, pp. 12-15. doi: 10.1109/LAGIRS48042.2020.9165559.
[45]
J. Lehtinen et al., " Noise2noise: Learning image restoration
without clean data, " 2018, arXiv:1803.04189.
[46] A. Krull, T.-O. Buchholz, and F. Jug, " Noise2void - Learning
denoising from single noisy images, " in Proc. IEEE/CVF Conf.
Comput. Vis. Pattern Recognit., 2019, pp. 2129-2137.
[47] S. Laine, T. Karras, J. Lehtinen, and T. Aila, " High-quality selfsupervised
deep image denoising, " in Proc. Adv. Neural Inform.
Process. Syst., 2019, pp. 6970-6980.
[48] A. Boulch, P. Trouvé, E. Koeniguer, F. Janez, and B. Le Saux,
" Learning speckle suppression in SAR images without ground
truth: Application to Sentinel-1 time-series, " in Proc. IEEE Int.
Geosci. Remote Sens. Symp., 2018, pp. 2366-2369, doi: 10.1109/
IGARSS.2018.8519370.
[49] X. Ma, C. Wang, Z. Yin, and P. Wu, " SAR image despeckling
by noisy reference-based deep learning method, " IEEE Trans.
Geosci. Remote Sens., vol. 58, no. 12, pp. 8807-8818, 2020, doi:
10.1109/TGRS.2020.2990978.
[50] E. Dalsasso, L. Denis, and F. Tupin, " SAR2SAR: A semi-supervised
despeckling algorithm for SAR images, " IEEE J. Sel. Topics
Appl. Earth Observ. Remote Sens., vol. 14, pp. 4321-4329, Apr.
2021, doi: 10.1109/JSTARS.2021.3071864.
[51] A. B. Molini, D. Valsesia, G. Fracastoro, and E. Magli, " Speckle2Void:
Deep self-supervised SAR despeckling with blindspot
convolutional neural networks, " IEEE Trans. Geosci.
Remote Sens., early access, 2021, doi: 10.1109/TGRS.2021
.3065461.
[52] A. B. Molini, D. Valsesia, G. Fracastoro, and E. Magli, " Towards
deep unsupervised SAR despeckling with blind-spot convolutional
neural networks, " 2020, arXiv:2001.05264.
[53] F. Sica, G. Gobbi, P. Rizzoli, and L. Bruzzone, " z-Net: Deep
residual learning for InSAR parameters estimation, " IEEE Trans.
Geosci. Remote Sens., vol. 59, no. 5, pp. 3917-3941, 2020, doi:
10.1109/TGRS.2020.3020427.
[54] L Denis, C Deledalle, and F Tupin, " From patches to deep learning:
Combining self-similarity and neural networks for SAR image
despeckling, " in Proc. IEEE Int. Geosci. Remote Sens. Symp.,
2019, pp. 5113-5116, doi: 10.1109/IGARSS.2019.8898473.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2022
IEEE Geoscience and Remote Sensing Magazine - June 2022

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2022
