We designed a complete acquisition-reconstruction frame-work to reduce the radiation dosage in 3D scanning transmission electron microscopy (STEM). Projection measurements are acquired by randomly scanning a subset of pixels at every tilt-view (i.e., random-beam STEM or “RB-STEM”). High-quality images are then recovered from the randomly down-sampled measurements through a regularized tomographic reconstruction framework. By fulfilling the compressed sensing requirements, the proposed approach improves the reconstruction of heavily-downsampled RB-STEM measurements over the current state-of-the-art technique. This development opens new perspectives in the search for methods permitting lower-dose 3D STEM imaging of electron-sensitive samples without degrading the quality of the reconstructed volume. A Matlab code implementing the proposed reconstruction algorithm has been made available online.
Chasing Mycobacteria10 Apr 2017
Once upon a time, a bachelor student started working on the problem of automating the analysis of time lapse sequences of mycobacteria as a summer job. Seven years, million lines of code and several sleepless nights later, two final-year PhD students are seriously planning a celebration party as the first results are getting out. To properly segment and track the bugs, a gigantic pipeline mixing image processing, graph theory, integer programming and machine learning will have been required. This talk recounts the story of this epic chase.
SIGGRAPH ASIA 201601 Nov 2016
Lévy's Persian summers18 Oct 2016
Algorithmic Aspects of Compressive Sensing03 Oct 2016
ICIP 201620 Sep 2016
Machine Vision forum in Heidelberg17 Aug 2016
ISBI 201606 Apr 2016
ICASSP 201615 Mar 2016
Decoding Epileptogenesis: A Dynamical System Approach09 Feb 2016