NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising
By Charles-Alban Deledalle, Loïc Denis, Florence Tupin, Andreas Reigber, Marc Jäger. IEEE Transactions on Geoscience and Remote Sensing, 2015
415 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geophysics
View Paper: https://www.semanticscholar.org/paper/98a2250e0a03719a891755a0069c91eb82eb15ef
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
- Richard Bamler
- Xiaoxiang Zhu
- Davide Cozzolino
- Giovanni Poggi
- Luisa Verdoliva
- Giovanni Chierchia
- Lichao Mou
In Computer Vision and Pattern Recognition
- Luisa Verdoliva
- Davide Cozzolino
- Giovanni Poggi
- Lichao Mou
- Xiaoxiang Zhu
- Richard Bamler
- Mohsin Ali
- Yuanyuan Wang
Citations in the corpus
- SAR image despeckling through convolutional neural networks (2017, methodology): “[7] C.-A. Deledalle, L. Denis, F. Tupin, A. Reigber, and M. Jäger, “NL-SAR: A unified nonlocal framework for resolutionpreserving (Pol)(In)SAR denoising,” IEEE Trans.”
- Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “A vast number of variations of the nonlocal method for SAR despeckling has been proposed, with the most notable ones included in [145], [146].”