Open postdoctoral position:
Discipline keywords: computational imaging, non-linear inverse problems, deep learning, convex and non-convex optimization, learning physical models, generative priors, compressive sensing, lensless endoscopy, astronomy
Location: Mathematical Engineering Department (INMA), ISPGroup, UCLouvain, Belgium.
Mobility criterion: This position is open to all nationalities, including Belgian, provided the candidate has spent less than 12 months over the last 3 years in Belgium.
The research group of Prof. Laurent Jacques in the Image and Signal Processing Group (ISPGroup) of UCLouvain of Louvain-la-Neuve in Belgium opens one position for a postdoctoral researcher to work on the new project "Learn2Sense: Learning Strategies for Computational Sensing" funded by the Belgian Fund for Scientific Research - FNRS.
Learn2Sense aims at creating physics-driven learning strategies for solving inverse problems encountered in modern computational imaging (CI) techniques.
Currently, CI is mainly driven by two co-existing approaches. First, the "analytical method" (AM) is based on our ability to: (AM1) develop an accurate forward model of the sensing process, i.e., from the interaction between the object and a probing signal (e.g., an electromagnetic field, a pressure wave) to the collection of the disturbed probing signal by the sensing device; (AM2) determine an accurate prior model on the structure of the object signal (such as sparsity or low-rank representation models); (AM3) efficiently compute the forward sensing model, i.e., to accurately reproduce the observed data if some representation of the object signal is known; and (AM4) to finally solve the related inverse problem --- often ill-posed or under-determined --- by restricting it to plausible solutions thanks to the prior model. As a result, AM-based computational sensing often targets simple, intrinsically linear or linearized forward models (as in tomographic applications) combined with linear prior models so that one can solve the related inverse problem with, e.g., convex optimization.
Second, the more recent “learning method” (LM) directly learns to invert the sensing model; it proposes to bypass AM1-AM4 thanks to the supervised training of deep neural networks. Thus, LM relies on the collection of large datasets coupling (known) object signals with their corresponding observations. However, while achieving unprecedented results in, e.g., image denoising, deconvolution or superresolution, these methods reach good accuracy only for sufficiently large learning datasets and are prone to instability under slight sensing model corruption. In addition, they do not provide interpretable models, e.g., allowing us to improve our knowledge of the acquisition physics or the sensing system.
This project lies at the frontier between the AM and LM approaches, reducing both the requirements drawn in AM1-AM4 and the need for large datasets. In particular, this project will pursue the following research directions:
These research directions will be fed both by a theoretical analysis of the proposed approaches, and by the design of novel computational sensing solutions for tomographic lensless endoscopy with ultrathin multicore optical fibers, as well as the analysis of new calibration and deconvolution techniques for astronomical imaging (such as direct telescopic imaging of exoplanets).
The project is associated with an existing collaborative network with local and international researchers specialized in deep learning, computational imaging, optics, astronomy and biology, namely, C. De Vleeschouwer and J. Lee (UCLouvain), O. Absil (ULiege), and H. Rigneault (Fresnel Institute, France).
In the general context described above, the postdoctoral researcher will more particularly focus his/her research for 2 years on one or several of the following topics:
More information about the project can be obtained upon request.
First application deadline on Friday, February 14th, 2020.
Applicants are requested to send the following documents (all in pdf):
Please send applications by email to:
Last updated March 03, 2020, at 07:41 AM