SAR despeckling via classification-based nonlocal and local sparse representation
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DOI: 10.1016/j.neucom.2016.09.028
Abstract: Abstract Nonlocally centralized sparse representation (CSR) is an effective approach for estimating original image in noise. In order to promote sparse coefficients more accurate than CSR, we present a new framework where another nonlocal sparsity… read more here.
Keywords: image; sparse; sparse representation; nonlocal sparsity ... See more keywords
A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images
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DOI: 10.1109/lgrs.2020.3034470
Abstract: In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical… read more here.
Keywords: practical solution; speckled speckled; solution sar; adversarial learning ... See more keywords
SAR Despeckling Using a Denoising Diffusion Probabilistic Model
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DOI: 10.1109/lgrs.2023.3270799
Abstract: Speckle is a type of multiplicative noise that affects all coherent imaging modalities including synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality and can adversely affect the performance of SAR… read more here.
Keywords: noise; diffusion probabilistic; denoising diffusion; probabilistic model ... See more keywords
Polarimetric SAR Despeckling With Convolutional Neural Networks
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DOI: 10.1109/tgrs.2022.3152068
Abstract: Coherent imaging systems such as synthetic aperture radar (SAR) are subject to speckle, the reduction of which is an active area of study. Methods based on deep convolutional neural networks (CNNs) have recently demonstrated state-of-the-art… read more here.
Keywords: sar; neural networks; polarimetric sar; polsar ... See more keywords