Publications associées à Edge-preserving smoothing

Maximum likelihood bolometry for ASDEX upgrade experiments

Olivier Claude Martin Février, Riccardo Rossi

Bolometry is an essential diagnostic for calculating the power balances and for the understanding of different physical aspects of tokamak experiments. The reconstruction method based on the Maximum Likelihood (ML) principle, developed initially for JET, h ...

Iop Publishing Ltd2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

On combining denoising with learning-based image decoding

Touradj Ebrahimi, Michela Testolina

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captu ...

SPIE2022

Coarse graining and large-N behavior of the d-dimensional N-clock model

Matthias Ruf, Matthias Benjamin Ruf

We study the asymptotic behavior of the N-clock model, a nearest neighbors ferromagnetic spin model on the d-dimensional cubic epsilon-lattice in which the spin field is constrained to take values in a discretization S-N of the unit circle S-1 consisting o ...

2021

Evaluating measurement uncertainty in Brillouin distributed optical fibre sensors using image denoising

Luc Thévenaz, Marcelo Alfonso Soto Hernandez, Zhisheng Yang, Simon Adrien Zaslawski

In 2016, our research team proposed in an issue of Nature Communications1 the use of multidimensional signal processing, especially image denoising techniques, to improve the signal-to-noise ratio (SNR) of distributed optical ﬁbre sensors. The beneﬁts of t ...

Nature Research2021

Deep Image Restoration: Between Data Fidelity and Learned Priors

Majed El Helou

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

EPFL2021

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Volkan Cevher, Cong Bang Vu

In this paper, we propose a novel splitting method for finding a zero point of the sum of two monotone operators where one of them is Lipschizian. The weak convergence the method is proved in real Hilbert spaces. Applying the proposed method to composite m ...

2021

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-gr ...

IEEE2020

Image Restoration using Plug-and-Play CNN MAP Denoisers

Sabine Süsstrunk, Siavash Arjomand Bigdeli, David Honzátko

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

SCITEPRESS2020

Graph-based image representation learning

Renata Khasanova

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

EPFL2019

Symplectic Model-Reduction with a Weighted Inner Product

Jan Sickmann Hesthaven, Babak Maboudi Afkham

In the recent years, considerable attention has been paid to preserving structures and invariants in reduced basis methods, in order to enhance the stability and robustness of the reduced system. In the context of Hamiltonian systems, symplectic model redu ...

2018

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

Wide-baseline object interpolation using shape prior regularization of epipolar plane images

Pascal Frossard, Christophe De Vleeschouwer, Thomas Maugey

This paper considers the synthesis of intermediate views of an object captured by two calibrated and widely spaced cameras. Based only on those two very different views, our paper proposes to reconstruct the object Epipolar Plane Image Volume [1] (EPIV), w ...

2017

Graph-Based Interpolation for Zooming in 3D Scenes

Pascal Frossard, Pinar Akyazi

In multiview systems, color plus depth format builds 3D representations of scenes within which the users can freely navigate by changing their viewpoints. In this paper we present a framework for view synthesis when the user requests an arbitrary viewpoint ...

2017

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Martin Kurt Storath

Mumford-Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising and segmentation. Being both non-smooth and non-convex, they are ...

Springer2016

Numerical approximation of the fractional Laplacian via hp-finite elements, with an application to image denoising

Jan Sickmann Hesthaven, Paolo Gatto

The fractional Laplacian operator (−∆)s on a bounded domain Ω can be realized as a Dirichlet-to-Neumann map for a degenerate elliptic equation posed in the semi-infinite cylinder Ω × (0,∞). In fact, the Neumann trace on Ω involves a Muckenhoupt weight that ...

Springer Verlag2015

Structured Sparse Coding for Image Denoising or Pattern Detection

Pascal Frossard, Sofia Karygianni

Sparsity has been one of the major drives in signal processing in the last decade. Structured sparsity has also lately emerged as a way to enrich signal priors towards more meaningful and accurate representations. In this paper we propose a new structured ...

2014

Anisotropic Interpolation of Sparse Generalized Image Samples

Michaël Unser, Aurélien Bourquard

Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Jean-Philippe Thiran, Meritxell Bach Cuadra, Subrahmanyam Venkata Ravi Mohana Sai Gorthi

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: “Global Weighted Shape-Based Averaging” (GWSBA) and “Local Weig ...

IEEE Institute of Electrical and Electronics Engineers2013

Improved Local Binary Pattern Based Action Unit Detection Using Morphological and Bilateral Filters

Jean-Philippe Thiran, Matteo Sorci, Anil Yuce

Automatic facial action unit (AU) detection in videos is the key ingredient to all systems that utilize a subject face for either interaction or analysis purposes. With the ever growing range of possible applications, achieving a high accuracy in the simpl ...

2013