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2007.02075
Cited By
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
4 July 2020
Andrea Bordone Molini
D. Valsesia
Giulia Fracastoro
E. Magli
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Papers citing
"Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks"
12 / 12 papers shown
Title
Bayesian Despeckling of Structured Sources
Ali Zafari
Shirin Jalali
148
0
0
21 Jan 2025
Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack
S. Mandelli
E. D. Cannas
Paolo Bestagini
Stefano Tebaldini
Stefano Tubaro
62
2
0
09 Jul 2024
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Chao Tao
Ji Qi
Mingning Guo
Qing Zhu
Haifeng Li
SSL
104
59
0
15 Nov 2022
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images
Inès Meraoumia
Emanuele Dalsasso
L. Denis
F. Tupin
45
1
0
22 Jul 2022
Multi-temporal speckle reduction with self-supervised deep neural networks
Inès Meraoumia
Emanuele Dalsasso
L. Denis
Rémy Abergel
F. Tupin
62
14
0
22 Jul 2022
Self-supervised Learning in Remote Sensing: A Review
Yi Wang
C. Albrecht
Nassim Ait Ali Braham
Lichao Mou
Xiao Xiang Zhu
159
228
0
27 Jun 2022
As if by magic: self-supervised training of deep despeckling networks with MERLIN
Emanuele Dalsasso
L. Denis
F. Tupin
66
68
0
25 Oct 2021
The potential of self-supervised networks for random noise suppression in seismic data
C. Birnie
M. Ravasi
T. Alkhalifah
Sixiu Liu
70
58
0
15 Sep 2021
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
Huanfeng Shen
Menghui Jiang
Jie Li
Chen Zhou
Qiangqiang Yuan
Liangpei Zhang
79
35
0
13 Aug 2021
A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks
A. Sebastianelli
M. P. D. Rosso
Silvia Liberata Ullo
Paolo Gamba
44
15
0
19 Apr 2021
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning
Emanuele Dalsasso
Inès Meraoumia
L. Denis
F. Tupin
49
4
0
01 Feb 2021
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro
E. Magli
Giovanni Poggi
Giuseppe Scarpa
D. Valsesia
L. Verdoliva
59
66
0
10 Dec 2020
1