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STUDENT PROJECTS

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Conditions:The projects are reserved for EPFL students or students of mobility program.

Ongoing projects

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Number of projects:3

Slice-based Dictionary Learning for Computed Tomography

Louis-Nicolas Douce
Master Semester Project: Summer 2020

In computed tomography (CT), the goal is to reconstruct a 3D object from a set of its 2D projections. Typically, this reconstruction task is formulated as an optimization problem where one also exploits certain properties of the signal of interest such as sparsity. Dictionary learning is a technique which uses training data to find a basis in which our signal can be represented in a sparse manner. Standard dictionary learning approaches are patch-based and thus computationally inefficient for 3D data. The goal of this project is to explore an alternate framework where the dictionary is based on 2D slices extracted from the volume. The project will be implemented in MATLAB.

Supervision:

Learning Robust Neural Networks via Controlling their Lipschitz Regularity

Moulik Choraria
Master Semester Project: Winter 2020

For adversarial robustness, it has been shown that augmenting adversarial perturbations during training, or adversarial training, makes the model more robust. However, it is computationally challenging to employ it on large-scale datasets. Adversarially trained models overfit to the specific attack type used for training, and the performance on unperturbed images drops. In addition, methods which improve robustness to non-adversarial corruptions are relatively less studied. Recently, we developed a framework for learning activations of deep neural networks with the motivation of controlling the global Lipschitz constant of the input-output relation. The goal of this project is to investigate the effect of our framework in the global robustness of the network within various setups. The student should have solid programming skills, in particular being familiar with PyTorch and a general understanding of the main concepts of deep learning.

Supervision:

Image reconstruction for optical diffraction tomography

Mohamed Bahroun
Master Semester Project: Summer 2020

Optical diffraction tomography (ODT) allows one to quantitatively measure the distribution of the refractive index (RI) of the sample [1]. It proceeds by measuring the complex fields that are produced when the sample is illuminated with plane waves from different angles. This allows for the deployment of numerical methods to recover the RI. In this work, we are interested in the limited-angle regime where only some angles are available, which makes the problem ill-posed. To overcome it, it is common to add prior knowledge (i.e., regularization) to the sample during the reconstruction such as non-negativity constraint. The project aims at implementing and evaluating a new regularization for ODT. The code will be done on Matlab within the GlobalBioIm library [2]. Good skill in Matlab is required. Please contact us for further details.

Reference
[1] Soubies, E., Pham, T. A., & Unser, M. (2017). Efficient inversion of multiple-scattering model for optical diffraction tomography. Optics express, 25(18), 21786-21800.
[2] Soubies, E., Soulez, F., Mccann, M. T., Pham, T. A., Donati, L., Debarre, T., ... & Unser, M. (2019). Pocket guide to solve inverse problems with globalbioim. Inverse Problems, 35(10), 104006.

Supervision:

2020 EPFL • webmaster.big@epfl.ch26.06.2020