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 …
SAR image despeckling using a convolutional neural network
Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise
known as speckle. Speckle makes the processing and interpretation of SAR images difficult …
known as speckle. Speckle makes the processing and interpretation of SAR images difficult …
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 …
Multi-objective CNN-based algorithm for SAR despeckling
Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is
largely used in applications, such as change detection, image restoration, segmentation …
largely used in applications, such as change detection, image restoration, segmentation …
SAR speckle removal using hybrid frequency modulations
S Liu, L Gao, Y Lei, M Wang, Q Hu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great
impact on subsequent processing and analysis operations. To remove speckle artifacts, this …
impact on subsequent processing and analysis operations. To remove speckle artifacts, this …
A deep neural network based on prior driven and structural-preserving for SAR image despeckling
C Lin, C Qiu, H Jiang, L Zou - IEEE Journal of Selected Topics …, 2023 - ieeexplore.ieee.org
Remarkable effectiveness has been demonstrated by deep neural networks in the
despeckling task for synthetic aperture radar (SAR) images. However, blurring and loss of …
despeckling task for synthetic aperture radar (SAR) images. However, blurring and loss of …
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 …
Ratio-based multitemporal SAR images denoising: RABASAR
In this paper, we propose a fast and efficient multitemporal despeckling method. The key
idea of the proposed approach is the use of the ratio image, provided by the ratio between …
idea of the proposed approach is the use of the ratio image, provided by the ratio between …
SAR despeckling using multi-objective neural network trained with generic statistical samples
Synthetic aperture radar (SAR) images are impaired by the presence of speckles. Despite
the deep interest of scholars in the last decades, SAR image despeckling is still an open …
the deep interest of scholars in the last decades, SAR image despeckling is still an open …