Sar Image Despeckling
10 papers with code • 0 benchmarks • 0 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
Benchmarks
These leaderboards are used to track progress in Sar Image Despeckling
Most implemented papers
SAR2SAR: a semi-supervised despeckling algorithm for SAR images
A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters.
SAR Image Despeckling Using a Convolutional Neural Network
Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle.
Learning a Dilated Residual Network for SAR Image Despeckling
qzhang95/SAR-DRN
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In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose 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).
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
Many different schemes have been proposed for the restoration of intensity SAR images.
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
diegovalsesia/speckle2void
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Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms.
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy.
As if by magic: self-supervised training of deep despeckling networks with MERLIN
We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex SAR images, called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a straightforward way to train all kinds of deep despeckling networks.
SAR Image Despeckling Using Continuous Attention Module
JK-the-Ko/SAR-CAM
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journal 2021
Although this architecture extracts features on different scales and has been shown to yield state-of-the-art performance, it still learns representation locally, resulting in missing overall information of convolutional features.
Transformer-based SAR Image Despeckling
Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult.
