Friday, July 19, 2013

PhD studentship: "Low-Dimensional Prior Models for Hyperspectral Images and Applications"

If you read this blog often, you know that hyperspectral imaging has a potentially very large economic impact. You may also have read most the entries on hyperspectral cameras and related issues. So if you are seeking a PhD studentship, you'll be ecstatic about the the following opportunity sent by Laurent Jacques for a PhD studentship in Louvain, Belgium::

Dear Igor,

Here is an new announcement for a PhD opening in my group in UCL on the general topic of
"Low-Dimensional Prior Models for Hyperspectral Images and Applications"

I think this could interest you and the readers of your blog, but feel free to publish it or not.

Briefly, this 4-year PhD project will be dedicated to the development of low-dimensional prior models for hyperspectral images. The main objectives are: to develop new compression schemes, to solve common data restoration problems of that field (deconvolution, missing/dead pixels, denoising) and to provide guidelines for the design of new hyperspectral sensors (e.g., to make them compressive with nice optics or coded aperture, or for super-resolution). This project will thus cover both the study important mathematical questions and the development of efficient algorithms for high dimensional inverse problems. Collaborations with international teams and industrials (developing hyperspectral optical sensors) are expected during this PhD thesis.

Detailed information on this new opening can be found here:

Best regards,

Prof. Laurent JACQUES
F.R.S.-FNRS Research Associate

Institute of Information and Communication Technologies,
Electronics and Applied Mathematics (ICTEAM/ELEN) - UCL
Batiment Stévin
Place du Levant 2, PO Box L5.04.04
1348 Louvain-la-Neuve, Belgium

Office: a.157

From the page:

Opening of a PhD position:

Low-Dimensional Prior Models for Hyperspectral Images and Applications
Deadline: September 1st, 2013.Starting: October 1st, 2013.Funded for 12-15 months, and to complete with Belgian NSF grant application.Project Summary: The objective of this project is to sustain the realization of a PhD thesis under the supervision of Prof. Laurent Jacques (ICTEAM, UCL) and with the help of Prof. Christophe De Vleeschouwer (ICTEAM, UCL) in the general topic of Low-Dimensional Prior Models for Hyperspectral Images and Applications.
More precisely, the project aims at efficiently representing the high-dimensional signal acquired with Hyperspectral imaging where a given object is observed in a dense set of wavelengths.The theoretical tools that will sustain these new models pertain to the general fields of computational harmonic analysis (or sparse analysis) [Mal99], low-rank data representations [Faz08], or hybrid models [Gol12]. Disposing of such an efficient hyperspectral data representation, i.e., a model which minimizes the number of parameters needed to represents spatio-spectral features, is of paramount importance for facing three different challenges targeted by this project:
  • Compression of hyperspectral data: the high-dimensionality of the hyperspectral data often involves subsequent compression methods. Currently, these hardly consider the geometry of the spatio-spectral domain. Most often each spectral band is compressed separately (e.g., using wavelet compression schemes) discarding the information contained in spectral correlations.
  • Non-linear hyperspectral data restoration: hyperspectral images suffer from several data corruptions that must be removed or reduced, such as noises (Poisson, Gaussian, or readout noises), missing informations (corrupted pixels, dead areas), instrumental response (or point-spread-function), subsampling (sensor with limited resolution) or data quantization/digitalization process (e.g., for storage or transmission needs).

  • Compressive hyperspectral imaging: the question of compressing hyperspectral data directly at the acquisition stage, as advocated by the compressed sensing theory [Can06], is of prime interest in order to improve common sensing procedures, e.g., to make them faster, more power efficient and/or with reduced communication levels. The decoding stage reconstructing compressively acquired data must also be as light as possible with the development of efficient algorithms [Com11].
The expected project achievements will have interesting impacts, for instance, in medical or in satellite imaging, where accurate characterization of skin diseases (e.g., melanoma) or of terrestrial areas (e.g., forests or soils) must be realized from their specific spectral signatures, or in automatic food quality control where spectral signature modifications are related to undesired food alterations.
  • [Can06] E. Candès, J. Romberg and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Comm. Pure and Appl. Math., 59(8):1207–1223, 2006. (pdf)
  • [Mal99] S. Mallat, “A Wavelet Tour of Signal Processing”. Academic Press, 1999.
  • [Gol12] M. Golbabaee, P. Vandergheynst. "Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery." IEEE ICASSP Int. Conf., 2012. (pdf)
  • [Com11] P. L. Combettes, J.-C. Pesquet. "Proximal splitting methods in signal processing." Fixed-Point Algorithms for Inverse Problems in Science and Engineering (2011): 185-212. (pdf)
  • [Faz08] M. Fazel, E. Candes, B. Recht, P. Parrilo, “Compressed sensing and robust recovery of low rank matrices”. In Sig., Syst. Comp., 42nd IEEE Asilomar Conf., pp. 1043-1047, 2008.
Job description:
The student will develop mathematical models and algorithms for the objectives described above. Research activity will be carried out in the Image and Signal Processing Group (ISP Group) in ICTEAM/ELEN, UCL, in collaborations with international teams (in Europe and USA) and industrial partners.
Candidate's Profile:
The position is reserved for candidate with very high profile (high grades). He/she must be graduated since no more than one year (funding requirement).
More particularly, we expect from him/her:
  • a M.Sc. in Applied Mathematics, Physics, Electrical Engineering, or Computer Science;
  • Knowledge (even partial) in the following topics constitutes assets:
  • Convex Optimization methods,
  • Signal/Image Processing,
  • Compressed Sensing and inverse problems.
  • Experience with Matlab, C and/or C++.
  • Good communications skills, both written and oral;
  • Speaking fluently in English (or French) is required. Writing in English is mandatory.
We offer:
  • A research position in a dynamic environment, working on leading-edge theories/methods/technologies with many international contacts.
  • The funding is granted for 12 to 15 months starting on October 1st, 2013.
  • During this first grant, the selected candidate will have to apply to a Belgian NSF grant (FNRS or FRIA) for completing his PhD program (4 years).
  • Salary is around 1600-1700 euros/month (netto, after taxes and social security coverage).
  • The candidate will have also to subscribe the standard PhD program of UCL.
Applications should include
  • a detailed resume,
  • copy of grade sheets for B.Sc. and M.Sc.
  • (if available, a pdf copy of one personal publication of interest or master project summary)
  • Names and complete addresses of referees are welcome.
Please send applications by email to (replace _AT_ and _DOT_):
laurent.jacques _AT_ uclouvain _DOT_ be
christophe.devleeschouwer _AT_ _uclouvain _DOT_ be
Questions about the subject or the position should be addressed to the same email addresses. 

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