André Jalobeanu

Centro de Geofísica de Évora
Universidade de Évora
Rua Romão Ramalho, 59
7002-554 Évora
Portugal

jalobeanu@uevora.pt

Home page

I have been working at CGE (University of Évora, Portugal) since March 2008. I was hired as a researcher (investigador auxiliar) in July 2009 within the Ciência 2008 program of the Portuguese Science Foundation (FCT).
From Jan 2005 to Feb 2008 I was a CNRS research scientist in France, where I worked in the PASEO group within the image processing team in the LSIIT lab near Strasbourg.
Before, I was with RIACS at NASA Ames research center (California, USA) from Jan 2002 to Dec 2004, with an INRIA postdoc fellowship during 2002. During that period, I was part of the Bayesian Vision Group led by P. Cheeseman, where I worked on 3D surface reconstruction of asteroids, wavelets on meshes and surface modeling.
Now my research projects include stereo disparity estimation, digital terrain model generation, LiDAR data processing, multisource image fusion and super-resolution. My main area is data processing and analysis (images, signals, time series) through Bayesian inference, and one of the priorities is the propagation and the evaluation of uncertainties. My research is application-oriented, and so far it has been motivated by various inverse problems in remote sensing, planetary sciences, Earth sciences and astronomy.
I am currently collaborating with D. Fitzenz, M. Ferry, C. Gama, D. Berry (University of Évora, Portugal), J.A. Gonçalves (University of Porto, Portugal), G. Gonçalves and J. Santos (University of Coimbra, Portugal), C. Collet and M. Petremand (LSIIT, France), F. Schmidt (University of Paris 11, France) and S. Hickman (USGS Menlo Park, USA).


Journal articles

2009	DOI M V Joshi, A Jalobeanu (2009) MAP estimation for Multiresolution Fusion in Remotely Sensed Images using an IGMRF Prior Model IEEE Trans. on Geoscience and Remote Sensing 48: 3. Jul Abstract: In this paper we propose a model based approach for the multiresolution fusion of satellite images. Given the high spatial resolution panchromatic (Pan) image and a low spatial and high spectral resolution multispectral (MS) image acquired over the same geographical area the problem is to generate a high spatial and high spectral resolution multispectral image. This is clearly an ill-posed problem and hence we need a proper regularization. We model each of the low spatial resolution MS images as the aliased and noisy versions of their corresponding high spatial resolution i.e., fused (to be estimated) MS images. A proper aliasing matrix is assumed to take care of the undersampling process. The high spatial resolution MS images to be estimated are then modeled as separate Inhomogeneous Gaussian Markov Random Fields (IGMRF) and a Maximum A Posteriori (MAP) estimation is used to obtain the fused image for each of the MS bands. The IGMRF parameters are estimated from the available high resolution Pan image and are used in the prior model for regularization purposes. Since the method does not directly operate on the Pan pixel values as most of the other methods do, the spectral distortion is minimum and the spatial properties are better preserved in the fused image as the IGMRF parameters are learned at every pixel. We demonstrate the effectiveness of our approach over some existing methods by conducting the experiments on synthetic data as well as on the images captured by the Quickbird satellite. Notes:
2008	A Jalobeanu, J A Gutiérrez, E Slezak (2008) Multisource data fusion and super-resolution from astronomical images Statistical Methodology 5: 4. Jul Abstract: Notes:
2007	D D Fitzenz, A Jalobeanu, S H Hickman (2007) Integrating Laboratory Creep Compaction Data With Numerical Fault Models : a Bayesian Framework Journal of Geophysical Research 112: B08410. Aug Abstract: Notes:
2006	A R Hajian, S M Movit, D Trofimov, B Balick, Y Terzian, K H Knuth, D Granquist-Fraser, K Huyser, A Jalobeanu, D McIntosh, A E Jaskot, S Palen, N Panagia (2006) An Atlas of [N II] and [O III] Images and Spectra of Planetary Nebulae Astrophysical Journal Supplement 169: Nov Abstract: Notes:
Book chapters

2009	L Blanc-Féraud, L Mugnier, A Jalobeanu (2009) Blind Image Deconvolution In: Inverse Problems in Vision and 3D Tomography Edited by:A Mohammad-Djafari. John Wiley and Sons isbn:9781848211728 Abstract: Notes: French version exists
	C Collet, F Flitti, S Bricq, A Jalobeanu (2009) Fusion and Multi-Modality In: Inverse Problems in Vision and 3D Tomography Edited by:A Mohammad-Djafari. John Wiley and Sons isbn:9781848211728 Abstract: Notes: French version exists
2007	A Jalobeanu, J Zerubia, L Blanc-Féraud (2007) Bayesian estimation of blur and noise in remote sensing imaging In: Blind image deconvolution: theory and applications Edited by:P Campisi, K Egiazarian. Taylor & Francis / CRC Press isbn:9780849373671 Abstract: We propose a Bayesian approach to infer the parameters of both blur and noise in remote sensing images. The modulation transfer function (MTF) of the imaging system, including atmosphere, optics and pixel-level sampling, is modeled by a parametric function with a small number of parameters. The noise is assumed to be white, additive and Gaussian. Both blur and noise processes are supposed to be stationary. To constrain this ill-posed inverse problem, the unknown scene is modeled by a scale-invariant stochastic process governed by a fractal exponent and a global energy term. The main novelty consists of treating all parameters as random variables whose mean is estimated within a fully Bayesian framework. The chosen approach can be summarized as the computation of the mean posterior marginal related to useful parameters only. This requires integrating the joint probability density function (PDF) with respect to all the nuisance parameters, which is achieved through Laplace approximations. In this chapter we present two approaches; the former is straightforward, and the latter leads to a more efficient, simplified and optimized estimation algorithm. In addition, we investigate methods of uncertainty estimation and model assessment, in order to validate our approach on real images and to propose further improvements. Notes:

Journal articles

Book chapters