Publications related to A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Deep Image Restoration: Between Data Fidelity and Learned Priors

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

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

EPFL2021

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Towards image denoising in the latent space of learning-based compression

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

In recent years, learning-based image compression has demonstrated similar or superior performance when com- pared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage ...

SPIE2021

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee, Xiaoyu Lin

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

2021

Towards Real-World Super-Resolution using Deep Neural Networks

Ruofan Zhou

Image super-resolution reconstructs a higher-resolution image from the observed low-resolution image. In recent years, machine learning models have been widely employed and deep learning networks have achieved state-of-the-art super-resolution performance. ...

EPFL2020

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Image Compression and Quality Assessment using Convolutional Neural Networks

Pinar Akyazi

The rapid development of digital imaging and video has placed visual contents in the heart of our lives. Digital multimedia span a vast number of areas from business to leisure, including but not limited to education, medicine, accessibility, training, adv ...

EPFL2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

EPFL2020

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

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Deep Image Restoration: Between Data Fidelity and Learned Priors

Ultrasound Imaging: From Physical Modeling to Deep Learning

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Towards image denoising in the latent space of learning-based compression

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Towards Real-World Super-Resolution using Deep Neural Networks

Robust Generalization via $\alpha$-Mutual Information

Image Compression and Quality Assessment using Convolutional Neural Networks

Robust Generalization via f−Mutual Information

Learning stereo reconstruction with deep neural networks

Learning stereo reconstruction with deep neural networks

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Robust Generalization via $\alpha$-Mutual Information

Robust Generalization via f−Mutual Information

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Towards Real-World Super-Resolution using Deep Neural Networks

Image Compression and Quality Assessment using Convolutional Neural Networks

Ultrasound Imaging: From Physical Modeling to Deep Learning

Towards image denoising in the latent space of learning-based compression

Deep Image Restoration: Between Data Fidelity and Learned Priors