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computer-vision-6

Sar Image Despeckling

3260 papers • 126 benchmarks • 313 datasets

Despeckling is the task of suppressing speckle from Synthetic Aperture Radar (SAR) acquisitions. Image credits: GRD Sentinel-1 SAR image despeckled with SAR2SAR-GRD

(Image credit: Papersgraph)

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Most implemented papers

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

F. Tupin, Emanuele Dalsasso, L. Denis•Thu Dec 31 2020

A deep learning algorithm with semi-supervision is proposed in this article: SAR2SAR, where Multitemporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions.

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Learning a Dilated Residual Network for SAR Image Despeckling

Qiangqiang Yuan, Qiang Zhang, Jie Li, Xiaoshuang Ma•Fri Sep 08 2017

A novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN), which shows superior performance over the state-of-the-art methods on both quantitative and visual assessments.

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SAR Image Despeckling Using a Convolutional Neural Network

Vishal M. Patel, He Zhang, Puyang Wang•Thu Jun 01 2017

A deep-learning-based approach called, image despeckling convolutional neural network (ID-CNN), for automatically removing speckle from the input noisy images, which achieves significant improvements over the state-of-the-art speckel reduction methods.

362 0

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SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy

F. Tupin, Wen Yang, Emanuele Dalsasso, L. Denis, Xiangli Yang•Sat Jun 27 2020

This paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data, and proposed methods are compared to other state-of-the-art speckled removal filters.

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Speckle2Void: Deep Self-Supervised SAR Despeckling With Blind-Spot Convolutional Neural Networks

D. Valsesia, Giulia Fracastoro, E. Magli, Andrea Bordone Molini•Fri Jul 03 2020

A self-supervised Bayesian despeckling method that can learn features of real SAR images rather than synthetic data and is superior on real data in both quantitative and visual assessments is proposed.

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As If by Magic: Self-Supervised Training of Deep Despeckling Networks With MERLIN

F. Tupin, Emanuele Dalsasso, L. Denis•Sun Oct 24 2021

A self-supervised strategy based on the separation of the real and imaginary parts of single-look complex (SLC) SAR images, called coMplex sElf-supeRvised despeckLINg (MERLIN), is introduced and it is shown that it offers a straightforward way to train all kinds of deep despekling networks.

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SAR Image Despeckling Using Continuous Attention Module

Jaekyun Ko, Sang-Hyun Lee•Fri Dec 31 2021

A new method is introduced for SAR image despeckling (SAR-CAM), which improves the performance of an encoder–decoder CNN architecture by using various attention modules and a context block is introduced at the minimum scale to capture multiscale information.

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Transformer-Based SAR Image Despeckling

Jeya Maria Jose Valanarasu, W. G. C. Bandara, Malsha V. Perera, Vishal M. Patel•Sat Jan 22 2022

The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions - aiding in better despekling.

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Sar Image Despeckling | State-of-the-Art