Publications related to A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

Towards Trustworthy Deep Learning for Image Reconstruction

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

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The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Privacy-Enhancing Optical Embeddings for Lensless Classification

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Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cam ...

2022

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Large Steps in Inverse Rendering of Geometry

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IEEE2021

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PERGAMON-ELSEVIER SCIENCE LTD2021

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

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Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...

EPFL2020

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

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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

A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

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Towards Trustworthy Deep Learning for Image Reconstruction

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Privacy-Enhancing Optical Embeddings for Lensless Classification

A first-order primal-dual method with adaptivity to local smoothness

Large Steps in Inverse Rendering of Geometry

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Passenger-centric timetable rescheduling: A user equilibrium approach

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Towards Trustworthy Deep Learning for Image Reconstruction

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

A first-order primal-dual method with adaptivity to local smoothness

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Privacy-Enhancing Optical Embeddings for Lensless Classification

Large Steps in Inverse Rendering of Geometry

Passenger-centric timetable rescheduling: A user equilibrium approach