Publications associées à Total variation denoising

Non-local Functionals Related to the Total Variation and Connections with Image Processing

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast ...

2018

Image and video denoising for distributed optical fibre sensors

Luc Thévenaz, Marcelo Alfonso Soto Hernandez

A technique based on a multi-dimensional signal processing approach is here described for performance enhancement of distributed optical fibre sensors. In particular, the main features of linear and nonlinear image denoising techniques are described for si ...

Ieee2017

Sparse Gradient Optimization and its Applications in Image Processing

Nikolaos Arvanitopoulos Darginis

Millions of digital images are captured by imaging devices on a daily basis. The way imaging devices operate follows an integral process from which the information of the original scene needs to be estimated. The estimation is done by inverting the integra ...

EPFL2017

Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization

Michaël Unser, Julien René Pierre Fageot, John Paul Ward

Splines come in a variety of flavors that can be characterized in terms of some differential operator L. The simplest piecewise-constant model corresponds to the derivative operator. Likewise, one can extend the traditional notion of total variation by con ...

2017

Dictionary Learning Based on Sparse Distribution Tomography

Michaël Unser, Pedram Pad

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

P. Pad2017

Insight gained from Principal Component Analysis in the analysis of river morphodynamics and associated sediment fluxes.

Christophe Ancey, Joris Heyman, Blaise Etienne Marceau Dhont

Bedload transport fluxes are known to widely fluctuate in both time and space (Ma et al., 2014). The typical scales of fluctuations are broad and often overlap: from random particle motions at the grain scale to large bed morphologies evolving at the river ...

2017

METHOD AND SYSTEM TO REDUCE NOISE IN PHASED-ARRAY SIGNALS FROM RECEIVERS LOCATED AT DIFFERENT LOCATIONS

Paul Hurley, Matthieu Martin Jean-André Simeoni, Nezihe Merve Gürel

The present invention is notably directed to a computerized method to reduce noise in phased- array signals from a set of receivers at different locations. Time-series are received from the receivers, which time-series form phased-array signals. The time-s ...

2017

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Non-local Functionals Related to the Total Variation and Connections with Image Processing

Hoài-Minh Nguyên

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

Total variation denoising

Simples variations. D’une station de ski à un site de production d’herbes alpines

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Image and video denoising for distributed optical fibre sensors

Sparse Gradient Optimization and its Applications in Image Processing

Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization

Dictionary Learning Based on Sparse Distribution Tomography

Non-convex, non-local functionals converging to the total variation

Insight gained from Principal Component Analysis in the analysis of river morphodynamics and associated sediment fluxes.

METHOD AND SYSTEM TO REDUCE NOISE IN PHASED-ARRAY SIGNALS FROM RECEIVERS LOCATED AT DIFFERENT LOCATIONS

Graph Chatbot

Chattez avec Graph Search

Sparse Gradient Optimization and its Applications in Image Processing

Dictionary Learning Based on Sparse Distribution Tomography

Simples variations. D’une station de ski à un site de production d’herbes alpines

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Non-convex, non-local functionals converging to the total variation

Image and video denoising for distributed optical fibre sensors

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization

Insight gained from Principal Component Analysis in the analysis of river morphodynamics and associated sediment fluxes.

METHOD AND SYSTEM TO REDUCE NOISE IN PHASED-ARRAY SIGNALS FROM RECEIVERS LOCATED AT DIFFERENT LOCATIONS