Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
By Gabriele Steidl, Tanja Teuber. Journal of Mathematical Imaging and Vision, 2010
295 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Mathematical Physics
View Paper: https://www.semanticscholar.org/paper/63075c226739d3a77b716d3a05dd601481de0fec
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
- Thomas Pock
- Antonin Chambolle
- Jean-Christophe Pesquet
- Laurent Condat
- Patrick L. Combettes
- Francis R. Bach
- Scott B. Lindstrom
- Brailey Sims
In Computer Vision and Pattern Recognition
- Thomas Pock
- Patrick L. Combettes
- Jean-Christophe Pesquet
- Antonin Chambolle
- Scott B. Lindstrom
- Brailey Sims
- Tiziano Bianchi
- Alessandro Lapini
In Mathematical Physics
- Thomas Pock
- Antonin Chambolle
- Jean-Christophe Pesquet
- Laurent Condat
- Patrick L. Combettes
- Francis R. Bach
- Tiziano Bianchi
- Fabrizio Argenti
Citations in the corpus
- An introduction to continuous optimization for imaging (2016, methodology): “Brox, A. Bruhn, N. Papenberg and J. Weickert (2004), High accuracy optical flow estimation based on a theory for warping.”
- Proximal Splitting Methods in Signal Processing (2009, methodology): “Applications of the Douglas–Rachford algorithm to signal and image processing can be found in [38, 52, 62, 63, 117, 118, 123].”
- SURVEY: SIXTY YEARS OF DOUGLAS–RACHFORD (2018, background): “convergence in their context and providing computational examples by denoising images and signals [133].”
- A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “When applying TV regularization in the logarithmic domain, convex TV problems can be obtained by applying different data fidelity terms, including the L2 norm [120], MAP based on Gamma distributed speckle [120], [124], a combination of the previous terms [121], the generalized Kullback-Leibler diver”
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (2011, unknown): “ADMM has also been applied to a number of statistical problems, such as constrained sparse regression [18], sparse signal recovery [70], image restoration and denoising [72, 154, 134], trace norm regularized least squares minimization [174], sparse inverse covariance selection [178], the Dantzig sel”
- Restoration of Poissonian Images Using Alternating Direction Optimization (2010, unknown): “Finally, the effectiveness of the resulting algorithm is illustrated in comparison with current state-of-the-art alternatives [11], [12], [19], [37], [39], via a set of experiments reported in Section VI.”