IEEE Geoscience and Remote Sensing Magazine - June 2021 - 51

image denoising, " Remote Sens., vol. 11, no. 6, p. 702, 2019. doi:
10.3390/rs11060702.
[87] F. Gu, H. Zhang, and C. Wang, " A two-component deep learning
network for SAR image denoising, " IEEE Access, vol. 8, pp. 17792-
17803, Jan. 2020. doi: 10.1109/ACCESS.2020.2965173.
[88] S. Liu et al., " SAR speckle removal using hybrid frequency modulations, "
IEEE Trans. Geosci. Remote Sens., early access, 2020.
doi: 10.1109/TGRS.2020.3014130.
[89] Y. Zhou, J. Shi, X. Yang, C. Wang, D. Kumar, S. Wei, and X.
Zhang, " Deep multi-scale recurrent network for synthetic aperture
radar images despeckling, " Remote Sens., vol. 11, no. 21,
p. 2462, 2019. doi: 10.3390/rs11212462.
[90] S. Vitale, D. Cozzolino, G. Scarpa, L. Verdoliva, and G. Poggi,
" Guided patchwise nonlocal SAR despeckling, " IEEE Trans.
Geosci. Remote Sens., vol. 57, no. 9, pp. 6484-6498, 2019. doi:
10.1109/TGRS.2019.2906412.
[91] C. 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.
[92] K. Zhang, W. Zuo, and L. Zhang, " FFDNet: Toward a fast and
flexible solution for CNN-based image denoising, " IEEE Trans.
Image Process., vol. 27, no. 9, pp. 4608-4622, 2017. doi: 10.1109/
TIP.2018.2839891.
[93] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger,
" Densely connected convolutional networks, " in Proc. IEEE Conf.
Comput. Vision Pattern Recognit. (CVPR), 2017, pp. 2261-2269.
[94] O. Ronneberger, P. Fischer, and T. Brox, " U-net: Convolutional
networks for biomedical image segmentation, " in Proc. Int.
Conf. Medical Image Comput. Comput.-Assisted Intervention (MICCAI),
2015, pp. 234-241.
[95] J. Johnson, A. Alahi, and F.-F. L, " Perceptual losses for realtime
style transfer and super-resolution, " in Proc. Eur. Conf. Comput.
Vision (ECCV), 2016, pp. 694-711.
[96] K. Simonyan and A. Zisserman, " Very deep convolutional networks
for large-scale image recognition, " in Proc. Int. Conf. Learning
Representations, 2015.
[97] M. Schmitt, L. Hughes, and X. Zhu, " The SEN1-2 dataset for deep
learning in SAR-optical data fusion, " ISPRS Ann. Photogrammetr.,
Remote Sens. Spatial Inform. Sci., vol. 4, no. 1, pp. 141-146, 2018.
[98] S. Lefkimmiatis, " Non-local color image denoising with convolutional
neural networks, " in Proc. IEEE Conf. Comput. Vision
Pattern Recognit. (CVPR), 2017, pp. 5882-5891.
[99] X. Wang, R. Girshick, A. Gupta, and K. He, " Non-local neural
networks, " in Proc. IEEE Conf. Comput. Vision Pattern Recognit.
(CVPR), 2018, pp. 7794-7803.
[100] C. Cruz, A. Foi, V. Katkovnik, and K. Egiazarian, " Nonlocalityreinforced
convolutional neural networks for image denoising, "
IEEE Signal Process. Lett., vol. 25, no. 8, pp. 1216-1220,
2018. doi: 10.1109/LSP.2018.2850222.
[101] L. Gomez, R. Ospina, and F. A, " Unassisted quantitative evaluation
of despeckling filters, " Remote Sens., vol. 9, no. 4, p. 389, Apr.
2017. doi: 10.3390/rs9040389.
[102] G. D. Martino, M. Poderico, G. Poggi, D. Riccio, and L. Verdoliva,
" Benchmarking framework for SAR despeckling, " IEEE
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1596-1615, 2014.
doi: 10.1109/TGRS.2013.2252907.
[103] K. Ravani, S. Saboo, and J. S. Bhatt, " A practical approach for
SAR image despeckling using deep learning, " in Proc. IEEE Int.
Geosci. Remote Sens. Symp. (IGARSS), 2019, pp. 2957-2960.
[104] Y. Yuan, J. Sun, and J. Guan, " Blind SAR image despeckling
using self-supervised dense dilated convolutional neural network, "
2019, arXiv:1908.01608.
[105] Y. Yuan, J. Guan, P. Feng, and Y. Wu, " A practical solution for
SAR despeckling with adversarial learning generated speckledto-speckled
images, " IEEE Geosci. Remote Sens. Lett., early access,
2020. doi: 10.1109/LGRS.2020.3034470.
[106] G. Zhang, Z. Li, X. Li, and Y. Xu, " Learning synthetic aperture
radar image despeckling without clean data, " J. Appl.
Remote Sens., vol. 14, no. 2, pp. 1-20, 2020. doi: 10.1117/1.
JRS.14.026518.
[107] E. Dalsasso, L. Denis, and F. Tupin, " SAR2SAR: A self-supervised
despeckling algorithm for SAR images, " 2020, arXiv:2006.15037.
[108]
S. Joo, S. Cha, and T. Moon, " DoPAMINE: Double-sided masked
CNN for pixel adaptive multiplicative noise despeckling, " in
Proc. AAAI Conf. Artif. Intell., 2019, vol. 33, pp. 4031-4038. doi:
10.1609/aaai.v33i01.33014031.
[109] A. Bordone Molini, D. Valsesia, G. Fracastoro, and E. Magli, " Towards
Deep unsupervised SAR despeckling with blind-spot convolutional
neural networks, " in Proc. IEEE Int. Geosci. Remote Sens.
Symp. (IGARSS), 2020, pp. 2507-2510.
[110] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras,
M. Aittala, and T. Aila, " Noise2noise: Learning image restoration
without clean data, " in Proc. Int. Conf. Mach. Learn., 2018,
pp. 2965-2974.
[111] J. Batson and L. Royer, " Noise2self: Blind denoising by self-supervision, "
in Proc. Int. Conf. Mach. Learn., 2019, pp. 524-533.
[112] A. Krull, T.-O. Buchholz, and F. Jug, " Noise2void-learning denoising
from single noisy images, " in Proc. IEEE Conf. Comput.
Vision Pattern Recognit. (CVPR), 2019, pp. 2129-2137.
[113] C. M. Stein, " Estimation of the mean of a multivariate normal
distribution, " Ann. Statist., vol. 9, no. 6, pp. 1135-1151, 1981.
doi: 10.1214/aos/1176345632.
[114] 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.
[115] X. Huang, Z. Xu, and J. Ding, " Video SAR image despeckling by
unsupervised learning, " IEEE Trans. Geosci. Remote Sens., early
access, 2020.
[116] 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.
[117] I. Bello, " Lambdanetworks: Modeling long-range interactions
without attention, " in Proc. Int. Conf. Learn. Representations, 2021.
[118] L. Verdoliva, R. Gaetano, G. Ruello, and G. Poggi, " Optical-driven
nonlocal SAR despeckling, " IEEE Geosci. Remote Sens. Lett., vol. 12,
no. 2, pp. 314-318, 2014. doi: 10.1109/LGRS.2014.2337515.
GRS
51
IEEE Geoscience and Remote Sensing Magazine - June 2021

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2021
