Fast Adaptive Elliptical Filtering Using Box Splines

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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

Wavefront shaping and deep learning in fiber endoscopy

Eirini Kakkava

Fiber endoscopy plays an important role in the clinical diagnosis and treatment processes involved in modern medicine. Thin fiber probes can relay information from confined places in the human body that are inaccessible for conventional bulky microscopes. ...

EPFL2020

RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis

Meritxell Bach Cuadra, Tobias Kober, Francesco La Rosa, Cristina Granziera, Hamza Kebiri, Po-Jui Lu

Objectives In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive d ...

2020

Adaptive phase correction of diffusion-weighted images

Jean-Philippe Thiran, Maxime Descoteaux, Marco Pizzolato

Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which fol ...

2020

Learning How To Recognize Faces In Heterogeneous Environments

Tiago De Freitas Pereira

Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades. Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, suc ...

EPFL2019

Clinical equivalence assessment of T2 synthesized pediatric brain magnetic resonance imaging

Tobias Kober, Tom Hilbert, Jocelyn Philippe

Background and purpose. - Automated synthetic magnetic resonance imaging (MRI) provides qualitative, weighted image contrasts as well as quantitative information from one scan and is well-suited for various applications such as analysis of white matter dis ...

MASSON EDITEUR2019

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019

Reparameterizing Discontinuous Integrands for Differentiable Rendering

Wenzel Alban Jakob, Guillaume Rémi Bruno Loubet

Differentiable rendering has recently opened the door to a number of challenging inverse problems involving photorealistic images, such as computational material design and scattering-aware reconstruction of geometry and materials from photographs. Differe ...

ASSOC COMPUTING MACHINERY2019

Learning to See through Reflections

Sabine Süsstrunk

"Pictures of objects behind a glass are difficult to interpret" "and understand due to the superposition of two real images: a reflection layer and a background layer. Separation of these two layers is challenging due to the ambiguities in as- signing text ...

IEEE2018

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

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