Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

By Charles-Alban Deledalle, Loïc Denis, Florence Tupin. IEEE Transactions on Image Processing, 2009

802 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e51f5e62e1ab45b9e0ae6b587f79a14dbc718ad2

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. Gabriele Steidl
5. Tanja Teuber
6. Richard Bamler
7. Xiaoxiang Zhu
8. Giovanni Poggi

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. Tiziano Bianchi
8. Alessandro Lapini

In Media Technology

1. Luisa Verdoliva
2. Davide Cozzolino
3. Vishal M. Patel
4. He Zhang
5. Puyang Wang
6. Gabriele Steidl
7. Tanja Teuber
8. Xiaoxiang Zhu

Citations in the corpus

• NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, methodology): “, to use the output of the denoising method as a pre-estimation of covariances, and apply again the nonlocal procedure using these refined covariances, as done in [35] and [36].”
• 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].”
• A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage (2012, methodology): “We compare the proposed technique with three state-ofthe-art despeckling algorithms: The spatially adaptive wavelet homomorphic shrinkage algorithm (SA-WBMMAE) [18], the wavelet-based MAP filtering algorithm (MAP-S) [22], and the PPB nonlocal filter [30].”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “[131] proposed one of the first nonlocal patchbased methods applied to speckle reduction by taking into account the statistical properties of speckle combined with the original nonlocal image denoising algorithm introduced”
• 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.”
• Removing Multiplicative Noise by Douglas-Rachford Splitting Methods (2010, methodology): “While we were preparing the final version of this paper, we got to know about the parallel work [22] of Deledalle et al., who propose an iterated non-local means filter with probabilistic patch-based weights adapted to SAR images.”
• 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.”