Three-Dimensional Optical Diffraction Tomography With Lippmann-Schwinger Model

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (31)

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Manana Lortkipanidze, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021

Human-Centered Scene Understanding via Crowd Counting

Weizhe Liu

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

EPFL2021

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Michaël Unser, Dimitris Perdios, Sébastien Alexandre Emery, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

2021

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

Vascular-targeted micelles as a specific MRI contrast agent for molecular imaging of fibrin clots and cancer cells

Anne-Sophie Chauvin, Vassily Vorobiev, Aurélien Thomas

Molecular medical imaging is intended to increase the accuracy of diagnosis, particularly in cardiovascular and cancer-related diseases, where early detection could significantly increase the treatment success rate. In this study, we present mixed micelles ...

ELSEVIER2021

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

Jean-Philippe Thiran, Meritxell Bach Cuadra, Cristina Granziera, Thomas Yu, Francesco La Rosa

Background and objective: Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion con ...

2021

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

Pretraining boosts out-of-domain robustness for pose estimation

Mackenzie Mathis, Alexander Mathis, Steffen Schneider, Thomas Ray Biasi, Mert Yüksekgönül, Matthias Bethge

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Touradj Ebrahimi, Pinar Akyazi

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

SPIE-INT SOC OPTICAL ENGINEERING2021

Adaptive Regularization For Three-Dimensional Optical Diffraction Tomography

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Ahmed Ayoub, Emmanuel Emilien Louis Soubies, Ahmed Bassam Sayed Ayoub Mohamed Emam

Optical diffraction tomography (ODT) allows one to quantitatively measure the distribution of the refractive index of the sample. It relies on the resolution of an inverse scattering problem. Due to the limited range of views as well as optical aberrations ...

IEEE2020

Full field dependence of primary aberrations in perturbed double-plane symmetric systems with a circular pupil

Toralf Scharf, Alessandro Grosso

This paper deals with the theory of primary aberrations for perturbed double-plane symmetric optical systems consisting of a combination of tilted and decentered surfaces and a circular pupil. First, the analytical expressions describing the full field beh ...

OPTICAL SOC AMER2020

Page 2 sur 2