Image restoration for remote sensing: Overview and toolbox
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
A review of generative adversarial networks (GANs) and its applications in a wide variety of disciplines: from medical to remote sensing
We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …
in a number of sectors. GANs combine two neural networks that compete against one …
SAR2SAR: A semi-supervised despeckling algorithm for SAR images
Speckle reduction is a key step in many remote sensing applications. By strongly affecting
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
MRDDANet: A multiscale residual dense dual attention network for SAR image denoising
Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle
noise, which results in the deterioration of image quality, so the removal of speckle in SAR …
noise, which results in the deterioration of image quality, so the removal of speckle in SAR …
Deep learning methods for synthetic aperture radar image despeckling: An overview of trends and perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-
dependent noise called speckle, which is very severe and may hinder image exploitation …
dependent noise called speckle, which is very severe and may hinder image exploitation …
As if by magic: self-supervised training of deep despeckling networks with MERLIN
Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR)
images. Speckle reduction has thus been the subject of numerous works spanning at least …
images. Speckle reduction has thus been the subject of numerous works spanning at least …
Underwater forward-looking sonar images target detection via speckle reduction and scene prior
H Long, L Shen, Z Wang, J Chen - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Forward-looking sonar (FLS) imagery system plays a significant role in oceanic object
recognition and detection since it can overcome the limitation of lighting conditions and …
recognition and detection since it can overcome the limitation of lighting conditions and …
Nonlocal CNN SAR image despeckling
We propose a new method for SAR image despeckling, which performs nonlocal filtering
with a deep learning engine. Nonlocal filtering has proven very effective for SAR …
with a deep learning engine. Nonlocal filtering has proven very effective for SAR …
SAR image despeckling by deep neural networks: From a pre-trained model to an end-to-end training strategy
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many
different schemes have been proposed for the restoration of intensity SAR images. Among …
different schemes have been proposed for the restoration of intensity SAR images. Among …