Dr. Jalal M. Fadili Profile

Dr. Jalal M. Fadili

at ENSICAEN

SPIE Involvement:

Conference Program Committee | Author

Publications (10)

PROCEEDINGS ARTICLE | September 26, 2013

Poisson noise removal with pyramidal multi-scale transforms

Proc. SPIE. 8858, Wavelets and Sparsity XV

KEYWORDS: Wavelet transforms, Chemical species, Wavelets, Denoising, Fourier transforms, Transform theory, Signal processing, Reconstruction algorithms, Picosecond phenomena, Galactic astronomy

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PROCEEDINGS ARTICLE | September 4, 2009

3D inpainting using sparse representations

Proc. SPIE. 7446, Wavelets XIII

KEYWORDS: Signal to noise ratio, Wavelet transforms, Chemical species, Video, Wavelets, Denoising, Transform theory, Data acquisition, Neodymium, 3D image processing

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PROCEEDINGS ARTICLE | September 4, 2009

Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond

Proc. SPIE. 7446, Wavelets XIII

KEYWORDS: Signal to noise ratio, Carbon dioxide, Data modeling, Matrices, Wavelets, Data processing, Associative arrays, Convolution, Mars, Data analysis

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PROCEEDINGS ARTICLE | September 4, 2009

Poisson denoising on the sphere

Proc. SPIE. 7446, Wavelets XIII

KEYWORDS: Wavelet transforms, Optical spheres, Data modeling, Wavelets, Denoising, Photons, Image filtering, Gamma radiation, Reconstruction algorithms, Spherical lenses

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PROCEEDINGS ARTICLE | September 3, 2009

Poisson noise removal in multivariate count data

Proc. SPIE. 7446, Wavelets XIII

KEYWORDS: Hyperspectral imaging, Astronomy, Wavelet transforms, Wavelets, Denoising, Photons, Linear filtering, Gamma radiation, Reconstruction algorithms, Binary data

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PROCEEDINGS ARTICLE | September 27, 2007

Activelets and sparsity: a new way to detect brain activation from fMRI data

Proc. SPIE. 6701, Wavelets XII

KEYWORDS: Signal to noise ratio, Statistical analysis, Wavelets, Interference (communication), Neuroscience, Hemodynamics, Associative arrays, Brain activation, Signal detection, Functional magnetic resonance imaging

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PROCEEDINGS ARTICLE | September 20, 2007

Learning adapted dictionaries for geometry and texture separation

Proc. SPIE. 6701, Wavelets XII

KEYWORDS: Image compression, Chemical species, Image processing, Wavelets, Denoising, Image restoration, Transform theory, Inverse problems, Associative arrays, Optimization (mathematics)

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PROCEEDINGS ARTICLE | September 20, 2007

Morphological diversity and sparsity: new insights into multivariate data analysis

Proc. SPIE. 6701, Wavelets XII

KEYWORDS: Signal to noise ratio, Data modeling, Chemical species, Image processing, Denoising, Image restoration, Inverse problems, Signal processing, Associative arrays, RGB color model

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Over the last few years, the development of multi-channel sensors motivated interest in methods for the coherent processing of multivariate data. From blind source separation (BSS) to multi/hyper-spectral data restoration, an extensive work has already been dedicated to multivariate data processing. Previous work has emphasized on the fundamental role played by sparsity and morphological diversity to enhance multichannel signal processing. Morphological diversity has been first introduced in the mono-channel case to deal with contour/texture extraction. The morphological diversity concept states that the data are the linear combination of several so-called morphological components which are sparse in different incoherent representations. In that setting, piecewise smooth features (contours) and oscillating components (textures) are separated based on their morphological differences assuming that contours (respectively textures) are sparse in the Curvelet representation (respectively Local Discrete Cosine representation). In the present paper, we define a multichannel-based framework for sparse multivariate data representation. We introduce an extension of morphological diversity to the multichannel case which boils down to assuming that each multichannel morphological component is diversely sparse spectrally and/or spatially. We propose the Generalized Morphological Component Analysis algorithm (GMCA) which aims at recovering the so-called multichannel morphological components. Hereafter, we apply the GMCA framework to two distinct multivariate inverse problems : blind source separation (BSS) and multichannel data restoration. In the two aforementioned applications, we show that GMCA provides new and essential insights into the use of morphological diversity and sparsity for multivariate data processing. Further details and numerical results in multivariate image and signal processing will be given illustrating the good performance of GMCA in those distinct applications.

PROCEEDINGS ARTICLE | November 13, 2003

Wavelet-based approaches for multiple hypothesis testing in activation mapping of functional magnetic resonance images of the human brain

Proc. SPIE. 5207, Wavelets: Applications in Signal and Image Processing X

KEYWORDS: Statistical analysis, Wavelets, Error analysis, Discrete wavelet transforms, Neuroimaging, Electronic filtering, Testing and analysis, Brain mapping, Functional magnetic resonance imaging, Brain

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PROCEEDINGS ARTICLE | September 25, 2003

An improved image segmentation approach based on level set and mathematical morphology

Proc. SPIE. 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition

KEYWORDS: Image processing algorithms and systems, Liver, Image segmentation, Image processing, Interfaces, Medical imaging, Digital imaging, Convolution, Mathematical morphology, 3D image processing

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Showing 5 of 10 publications

Conference Committee Involvement (4)

Wavelets and Sparsity XVI

10 August 2015 | San Diego, California, United States

Wavelets and Sparsity XV

26 August 2013 | San Diego, California, United States

Wavelets XIII

2 August 2009 | San Diego, California, United States

Wavelets XII

26 August 2007 | San Diego, California, United States

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