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On going


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

Ongoing projects

All Project

Number of projects:8

Framework for pixel classification system in time-lapse microscopy images

Julien Marengo
Master Semester Project: Winter 2017


Design of optimal 2D-steerable filters: theory and implementation

Benjamin Beck
Master Diploma Project: Summer 2017


Sparse steerable textons

Jeremy Casorso
Master Semester Project: Summer 2017


Interactive B-spline demo running in a web browser

Robin Lang
Bachelor Semester Project: Winter 2016

In this project the student creates an interactive web-demo application using web-mathematica ( Plots of 1D signals that are constructed with B-splines are created and displayed along with the weighted shifted basis functions. Interactively derivatives or integrals are computed and also displayed as a function of the derivatives or integrals of the B-splines. The student should have excellent programming skills (IC section) and be familiar with HTML5, java, javascript. Additional experience with mathematica is a plus.


Didactic demonstrations for image-processing courses in HTML5 / Javascript

Cyril Favre
Bachelor Semester Project: Winter 2016

Availability of interactive tools is essential for assisting students in visualization and understanding of the image-processing concepts. The latest technological advances, in particular HTML5 and Javascript, offer an opportunity to develop demos of basic operations in image-processing, like digital filters morphological operators, edge detection or feature detections. The goal of this project is to build a generic environment for Internet allowing development of a set of interactive demos. At the end, the work will be made freely available through our web server.


Region of Interest Computed Tomography

Vilaclara, Laura
Master Semester Project: Summer 2016

In this project, we aim to develop a method for region of interest (ROI) computed tomography (CT) reconstruction. In X-ray CT, the images of slices of an object are reconstructed from a set of X-ray images of the object taken from different angles. This technique has found applications in a broad range of areas including materials science engineering, archaeology, biology, and medicine. Especially in biomedical applications, the user is often interested only in a small ROI inside a larger volume, however, it is non-trivial in CT to reconstruct anything but the entire field of view because objects outside the ROI cause artifacts in the ROI reconstruction. We want to explore ways of creating a high-resolution reconstruction of an ROI by using a low-resolution full field of view reconstruction to correct for these artifacts.


Deep Learning for Medical Imaging (MRI)

Christophe Windler
Master Semester Project or Master Diploma Project: Winter 2016

Accelerated MRI allows important reduction of the acquisition time (~5 times faster). The “killer application” for accelerated MRI is dynamic 3D cardiac MRI, as the shortening of the acquisition time reduces the impact of undesirable motion artifacts. The most recent accelerated MRI methods rely on various optimization methods, which are unfortunately computationally demanding and require tuning of some optimization parameters. Nowadays, convolutional neural network (CNN) are very popular solvers for various inverse problems. The performance and speed of reconstruction by CNN are noticeably improved over conventional iterative optimization methods. Yet, there still lacks CNN approaches for reconstruction of accelerated MRI, especially for multi-dimensional data. In this project, the student's task will be to explore a CNN-based reconstruction method for accelerated MRI. The implementation will take advantage of the MatConvNet toolbox of MATLAB or Caffe toolbox of C/Python (preferred).


Super-resolution imaging using Deep-Learning

Clémentine Aguet
Master Semester Project: Winter 2016

Structured Illumination Microscopy (SIM) allows us to improve the resolution of classical wide-field imaging systems by moving high-frequency components into the observable microscope region. The flexibility of SIM regarding fluorescent probes and its low illumination power requirement makes it extremely interesting for life cell imaging which is of a fundamental importance in biological research. An attractive SIM setting from the acquisition simplicity viewpoint involves the use of speckle illumination patterns. However, such a system is mainly limited by the large number of acquisitions required by the current reconstruction methods and the fact that the illumination patterns are unknown. In this project, the student will investigate the use of convolutional neural network (CNN) to the chalenging goal of both estimating the illumination patterns and reconstructing the super-resolved image from a reduced number of acquired speckle SIM images.


2017 EPFL • webmaster.big@epfl.ch04.04.2017