Compressive video acquisition for hyperspectral endoscopy
CREATIS opens a Master internship of 5-6 months to address new questions in the emerging field of fast single-pixel video recovery.
Keywords : Computational imaging, compressive video acquisition, image processing, machine learning, experimental setup.
Background : Recent advances in signal processing have made it possible to design new digital imaging systems. Single-pixel imaging is a new paradigm that enables two-dimensional imaging from a point detector. It has been raising increasing attention because it allows high-performance optical imaging systems (e.g., hyperspectral and/or time-resolved) to be developed at very lowcost . Single-pixel cameras comprise a single point detector that is coupled with a spatial light modulator. By performing a sequence of optical measurements for different modulationpatterns, it is possible to recover the image of the observed scene provided that ad-hoc restoration algorithms are implemented. Our group recently showed that adapting the patterns tothe object can significantly improve the recovery of still images [2-3].
Work Plan : The aim of this project is to i) generalize the concept of pattern adaptation to video reconstruction and ii) demonstrate its effectiveness on experimental hyperspectral data that areacquired in endoscopy. The successful candidate will update our current experimental set-up. In particular, he will couple a coherent optical bundle to both a conventional camera and aspectrometer. This will enable to acquire hyperspectral data through an endoscopic channel, with the control of RGB video flow.
Context : The expected duration of the internship is 5-6 months.
Skills : We are looking for an enthusiastic student with a background in optics, willing to conceive and develop a new instrumentation. Programming language: Labview, Matlab.
Salary : ~550€ net monthly
How to apply? : Send your CV, a motivation letter, and your academic records to Nicolas Ducros, Raphaël Sablong, Bruno Montcel (see "contacts")
References : R. G. Baraniuk et al., “Compressive video sensing: Algorithms, architectures, and applications”, IEEE Signal Processing Magazine, 34 (1), 52-66, 2017.
 F. Rousset et al. “Adaptive basis scan by wavelet prediction for single-pixel imaging”, IEEE Transactions on Computational Imaging, 3 (1), 36-46, 2017. (Open access pdf)