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Concept

Total variation denoising

Related publications (30)

A generic diffusion-based approach for 3D human pose prediction in the wild

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach th ...

IEEE2023

AN UNSUPERVISED METHOD FOR THE DETECTION OF AND TRACKING OF TARGETS IN SPOTLIGHT MODE SAR IMAGES

Lloyd Haydn Hughes

Taking advantage of Capella's ability to dwell on a target for an extended period of time (nominally 30s) in its spotlight (SP) mode, an unsupervised methodology for detecting moving targets in this data is presented in this paper. By colourizing short seg ...

IEEE2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina

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

Regularization of polynomial networks for image recognition

Volkan Cevher, Grigorios Chrysos, Bohan Wang

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

2023

Regularization of polynomial networks for image recognition

Volkan Cevher, Grigorios Chrysos, Bohan Wang

Ieee Computer Soc2023

The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems

Michaël Unser, Rahul Parhi

The usual explanation of the efficacy of wavelet-based methods hinges on the sparsity of many real-world objects in the wavelet domain. Yet, standard wavelet-shrinkage techniques for sparse reconstruction are not competitive in practice, one reason being t ...

IEEE Institute of Electrical and Electronics Engineers2023

DiGress: Discrete Denoising diffusion for graph generation

Pascal Frossard, Volkan Cevher, Igor Krawczuk, Clément Arthur Yvon Vignac, Bohan Wang

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...

2023

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

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

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Nicolas Henri Bernard Flammarion

We consider the problem of sampling from a density of the form p(x) ? exp(-f (x) - g(x)), where f : Rd-+ R is a smooth function and g : R-d-+ R is a convex and Lipschitz function. We propose a new algorithm based on the Metropolis-Hastings framework. Under ...

Microtome Publishing2022

Image Denoising with Control over Deep Network Hallucination

Sabine Süsstrunk, Majed El Helou

Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and ge ...

Society for Imaging Science and Technology (IS&T)2022

Random Surface Covariance Estimation by Shifted Partial Tracing

Victor Panaretos, Tomas Masák

The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even ...

2023

Lightweight HI source finding for next generation radio surveys

Jean-Paul Richard Kneib, Frédéric Courbin, Aymeric Alexandre Galan, Austin Chandler Peel, Emma Elizabeth Tolley, Mark Thomas Sargent

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modu ...

ELSEVIER2022

Penalized denoising of vehicle trajectories collected by a swarm of drones

Nikolaos Geroliminis, Emmanouil Barmpounakis, Georgios Anagnostopoulos

Vehicle trajectory datasets collected in urban traffic environments with drones pose unique chal- lenges in terms of denoising due to extensive visual restrictions, perspective distortions and human- induced errors. This article taps into the unexplored po ...

2022

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

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

Sparse non-negative super-resolution - simplified and stabilised

Armin Eftekhari, Hemant Tyagi

We consider the problem of non-negative super-resolution, which concerns reconstructing a non-negative signal x = Sigma(k )(i=1)a(i)delta(ti) from m samples of its convolution with a window function phi(s - t), of the form y(s(j)) = Sigma(k)(i=1) a(i) phi( ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

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

Functional registration and local variations: Identifiability, rank, and tuning

Victor Panaretos, Anirvan Chakraborty

We develop theory and methodology for the problem of nonparametric registration of functional data that have been subjected to random deformation (warping) of their time scale. The separation of this phase variation ("horizontal" variation) from the amplit ...

2021

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

Sabine Süsstrunk, Majed El Helou

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for sp ...

2021

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