Dr. Jalal M. Fadili Profile

Dr. Jalal M. Fadili

at ENSICAEN

SPIE Involvement:

Author

Proceedings Article | 12 September 2021 Presentation

Roughness measurements at one scattering direction with white light

Xavier Buet, Claude Amra, Myriam Zerrad, Michel Lequime, Jalal Fadili, Jean-Jacques-Narcisse Godeme

Proceedings Volume 11872, 118720V (2021) https://doi.org/10.1117/12.2597030

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Scattering techniques are today well controlled to characterize roughnesses of high-precision substrates and coatings, which are around a fraction of a nanometer in the optical bandwidth [1, 2, 3]. All these techniques involve a receiver, sample or beam motion so as to record the whole angular scattering pattern by reflection and transmission. However, in some situations these angular motions are penalising. This is the case when fast roughness-measurements are required (on-line measurements), or when the sample cannot be displaced (case of large pieces), or when only one scattering direction is allowed (case where the sample cannot be separated from a system) … For these reasons we recently proposed proposed [4] an alternative which consisted in using white light so as to cancel any mechanical movement in a scattering system. The resulting system is a one-angle white-light scatterometer with a reduced spatial frequency bandwidth. The principle of this white light scatterometer relies on the wavelength-angle equivalence in the spatial frequency. However additional concepts must be introduced to make the white light scattering to be proportional to roughness. Actually we have to shape the wavelength spectrum of illumination in a specific way given by theory. Two experimental techniques are presented to shape the incident spectrum. The first [4] is based on an interferential filter but suffers some disadvantages, such as the absence of tunability or retroaction. The second technique [5] involves micro-mirror or LCD matrices coupled with gratings, and offers several advantages. Fast retroaction allows to take account of a shift in the source power, and the tunability also offers the opportunity to build quasi-arbitrary filters. This last remark allows to extract a series of roughness moments (not only the roughness), so that the autocorrelation function of the topography can be reconstructed. We will discuss advantages and limits of this new technique.

Proceedings Article | 26 September 2013 Paper

Poisson noise removal with pyramidal multi-scale transforms

Arnaud Woiselle, Jean-Luc Starck, Jalal Fadili

Proceedings Volume 8858, 88581D (2013) https://doi.org/10.1117/12.2023315

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

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Proceedings Article | 4 September 2009 Paper

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

J. Bobin, Y. Moudden, J.-L. Starck, J. Fadili

Proceedings Volume 7446, 74461D (2009) https://doi.org/10.1117/12.826131

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

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Proceedings Article | 4 September 2009 Paper

Poisson denoising on the sphere

J. Schmitt, J. L. Starck, J. Fadili, I. Grenier, J. M. Casandjian

Proceedings Volume 7446, 74461A (2009) https://doi.org/10.1117/12.824446

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

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Proceedings Article | 4 September 2009 Paper

3D inpainting using sparse representations

A. Woiselle, J.-L. Starck, J. Fadili

Proceedings Volume 7446, 74461C (2009) https://doi.org/10.1117/12.825644

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

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Proceedings Article | 3 September 2009 Paper

Poisson noise removal in multivariate count data

J. Fadili, J.-L. Starck, B. Zhang, S. Digel

Proceedings Volume 7446, 74461B (2009) https://doi.org/10.1117/12.825063

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

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Proceedings Article | 27 September 2007 Paper

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

Ildar Khalidov, Dimitri Van De Ville, Jalal Fadili, Michael Unser

Proceedings Volume 6701, 67010Y (2007) https://doi.org/10.1117/12.734706

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

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Proceedings Article | 20 September 2007 Paper

Morphological diversity and sparsity: new insights into multivariate data analysis

J. Bobin, J. Fadili, Y. Moudden, J.-L. Starck

Proceedings Volume 6701, 67011U (2007) https://doi.org/10.1117/12.731589

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

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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 | 20 September 2007 Paper

Learning adapted dictionaries for geometry and texture separation

Gabriel Peyré, Jalal Fadili, Jean-Luc Starck

Proceedings Volume 6701, 67011T (2007) https://doi.org/10.1117/12.731244

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

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Proceedings Article | 13 November 2003 Paper

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

Jalal Fadili, Edward Bullmore

Proceedings Volume 5207, (2003) https://doi.org/10.1117/12.503377

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

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Proceedings Article | 25 September 2003 Paper

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

Hua Li, Abderrahim Elmoataz, Jaral Fadili, Su Ruan

Proceedings Volume 5286, (2003) https://doi.org/10.1117/12.538710

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

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Showing 5 of 11 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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