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Publications associées (29)

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

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

ExprADA: Adversarial domain adaptation for facial expression analysis

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...

2020

Imaging from Echoes: On Inverse Problems in Ultrasound

Adrien Georges Jean Besson

Ultrasound (US) imaging is currently living a revolution. On the one hand, ultrafast US imaging, a novel way of acquiring and producing US images, has paved the way to several advanced imaging modes, e.g. shear-wave elastography, ultrafast Doppler imaging ...

EPFL2018

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of 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-grounded ...

2019

Kernel Modeling Super-Resolution on Real Low-Resolution Images

Sabine Süsstrunk, Ruofan Zhou

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their per ...

IEEE COMPUTER SOC2019

Deep Convolutional Neural Network for Ultrasound Image Enhancement

Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Marcel Arditi, Adrien Besson, Florian Martinez, Manuel Vonlanthen

The problem of improving image quality in ultrafast ultrasound (US) imaging by means of regularized iterative algorithms has raised a vast interest in the US community. These approaches usually rely on standard image processing priors, such as wavelet spar ...

IEEE2018

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

EPFL2018

Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion

Bogdan Draganski

Purpose: Head movements are a major source of MRI artefacts. Prospective motion correction techniques significantly improve data quality, but strong motion artefacts may remain in the data. We introduce a framework to suspend data acquisition during period ...

Wiley2018

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