A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

By Sara Parrilli, Mariana Poderico, Cesario Vincenzo Angelino, Luisa Verdoliva. IEEE Transactions on Geoscience and Remote Sensing, 2012

708 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/713067dc294f87d3a4959a418f047b06ced11eb7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Davide Cozzolino
2. Vishal M. Patel
3. Luisa Verdoliva
4. Richard Bamler
5. Xiaoxiang Zhu
6. Giovanni Poggi
7. Giovanni Chierchia
8. He Zhang

In Computer Vision and Pattern Recognition

1. Luisa Verdoliva
2. Davide Cozzolino
3. Vishal M. Patel
4. He Zhang
5. Giovanni Poggi
6. Puyang Wang
7. Xiaoxiang Zhu
8. Giovanni Chierchia

In Media Technology

1. Luisa Verdoliva
2. Davide Cozzolino
3. Vishal M. Patel
4. He Zhang
5. Puyang Wang
6. Xiaoxiang Zhu
7. Giovanni Poggi
8. Tiziano Bianchi

Citations in the corpus

• SAR Image Despeckling Using a Convolutional Neural Network (2017, methodology): “Note that [15], [20], [24], and [25] are the most recent state-of-the-art image restoration algorithms.”
• SAR image despeckling through convolutional neural networks (2017, methodology): “Wavelet shrinkage [2], sparse representations [3], and especially nonlocal filtering [4, 5, 6, 7], represent arguably the current state-of-the-art.”
• A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “Nonlocal Filtering– Among the despeckling methods that cannot be included in the classical Bayesian framework, nonlocal (NL) filtering is surely one of the most interesting and promising solutions [108], [109].”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “introduced SUBMITTED TO IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2020 10 Fig. 6: The Architecture of CNN for SAR image despeckling [52]. Fig. 7: The comparison of speckle reduction among SAR-BM3D [119], SAR-CNN [52], and CNN-NLM applied to a small strip of COSMO-SkyMed data over Caserta, Italy, ”
• NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, methodology): “The second considered state-of-the-art method is the recent SAR-BM3D technique [38].”