Dr. Alain Dieterlen Profile

Dr. Alain Dieterlen

at IUT de Mulhouse

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Author

Publications (14)

Proceedings Article | 28 July 2023 Paper

Enhanced key-point detection for plenoptic imaging

M. Al Assaad, S. Bazeille, T. Josso-Laurain, A. Dieterlen, C. Cudel

Proceedings Volume 12749, 127490T (2023) https://doi.org/10.1117/12.2692367

KEYWORDS: Cameras, Visualization, Microlens, Calibration, Interpolation, Image enhancement, Depth maps, Sensors

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SPIE Journal Paper | 22 September 2021

Homography-based model with light calibration for plenoptic cameras

Mohamad Al Assaad, Stéphane Bazeille, Thomas Josso-Laurain, Alain Dieterlen, Christophe Cudel

OE, Vol. 60, Issue 09, 095103, (September 2021) https://doi.org/10.1117/12.10.1117/1.OE.60.9.095103

KEYWORDS: Cameras, Calibration, 3D modeling, Sensors, Optical engineering, Error analysis, Image sensors, 3D acquisition, Visual process modeling, Optimization (mathematics)

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Proceedings Article | 16 July 2021 Paper

Interest of pseudo-focused images for key-points detection in plenoptic imaging

M. Al Assaad, S. Bazeille, T. Laurain, A. Dieterlen, C. Cudel

Proceedings Volume 11794, 1179405 (2021) https://doi.org/10.1117/12.2588954

KEYWORDS: Cameras, Convolutional neural networks, 3D image processing, 3D vision, Spatial resolution, Image analysis

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Proceedings Article | 11 May 2012 Paper

Line matching for automatic change detection algorithm

Jérôme Dhollande, David Monnin, Laetitia Gond, Christophe Cudel, Sophie Kohler, Alain Dieterlen

Proceedings Volume 8357, 83571L (2012) https://doi.org/10.1117/12.919856

KEYWORDS: Signal detection, Cameras, Image registration, Improvised explosive devices, Roads, Video, Detection and tracking algorithms, Image processing, Global Positioning System, Signal processing

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Proceedings Article | 3 May 2011 Paper

Comparative study of heart sound localization algorithms

A. Moukadem, A. Dieterlen, N. Hueber, C. Brandt, P. Raymond

Proceedings Volume 8068, 80680P (2011) https://doi.org/10.1117/12.887072

KEYWORDS: Heart, Interference (communication), Vacuum fluorescent displays, Neural networks, Pathology, Receivers, Fractal analysis, Acoustics, Signal processing, Prototyping

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Proceedings Article | 4 May 2010 Paper

Analysis and quantification of laser-dazzling effects on IR focal plane arrays

N, Hueber, D, Vincent, A, Morin, A, Dieterlen, P, Raymond

Proceedings Volume 7660, 766042 (2010) https://doi.org/10.1117/12.850236

KEYWORDS: Staring arrays, Cameras, Pulsed laser operation, Picosecond phenomena, Infrared cameras, Contrast transfer function, Laser countermeasures, Optoelectronics, Infrared imaging, Laser irradiation

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Proceedings Article | 25 April 2008 Paper

Fast deconvolution with non-invariant PSF for 3-D fluorescence microscopy

Elie Maalouf, Bruno Colicchio, Alain Dieterlen

Proceedings Volume 7000, 70001K (2008) https://doi.org/10.1117/12.781400

KEYWORDS: Point spread functions, Deconvolution, Convolution, Luminescence, Microscopes, Image processing, Fourier transforms, Microscopy, 3D image processing, Objectives

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Proceedings Article | 22 April 2008 Paper

Recent advances in 3-D fluorescence microscopy: tomography as a source of information

A. Dieterlen, M. Debailleul, A. De Meyer, B. Simon, V. Georges, B. Colicchio, O. Haeberlé, V. Lauer

Proceedings Volume 7008, 70080S (2008) https://doi.org/10.1117/12.796995

KEYWORDS: Point spread functions, Luminescence, Microscopes, Microscopy, Deconvolution, Tomography, 3D image processing, Image processing, 3D acquisition, Optical microscopy

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3-D optical fluorescent microscopy becomes now an efficient tool for volume investigation of living biological samples. Developments in instrumentation have permit to beat off the conventional Abbe limit, in any case the recorded image can be described by the convolution equation between the original object and the Point Spread Function (PSF) of the acquisition system. If the goal is 3-D quantitative analysis, whether you improve the instrument capabilities, or (and) you restore the data. These last is until now the main task in our laboratory. Based on the knowledge of the optical Transfer Function of the microscope, deconvolution algorithms were adapted to automatic determine the regularisation threshold in order to give less subjective and more reproducible results. The PSF represents the properties of the image acquisition system; we have proposed the use of statistical tools and Zernike moments to describe a 3-D system PSF and to quantify the variation of the PSF. This first step toward standardization is helpful to define an acquisition protocol optimizing exploitation of the microscope depending on the studied biological sample. We have pointed out that automating the choice of the regularization level; if it facilitates the use, it also greatly improves the reliability of the measurements. Furthermore, to increase the quality and the repeatability of quantitative measurements a pre-filtering of images improves the stability of deconvolution process. In the same way, the PSF pre-filtering stabilizes the deconvolution process. We have shown that Zernike polynomials can be used to reconstruct experimental PSF, preserving system characteristics and removing the noise contained in the PSF. Fluorescent microscopes suffer from limitations; photobleaching and phototoxicity effects, or influence of the sample optical properties to 3-D observation. Amplitude and phase of the object can be reached with optical tomography based on a combination of microholography with a tomographic illumination. So indices cartography of the specimen can be obtained, and combined with fluorescence information it will open new possibilities in 3-D optical microscopy.

Proceedings Article | 14 June 2006 Paper

Image processing tools dedicated to quantification in 3D fluorescence microscopy

A. Dieterlen, A. De Meyer, B. Colicchio, S. Le Calvez, O. Haeberlé, S. Jacquey

Proceedings Volume 6254, 62540O (2006) https://doi.org/10.1117/12.679922

KEYWORDS: Point spread functions, Deconvolution, Luminescence, Microscopes, Image processing, 3D acquisition, 3D image processing, Microscopy, Image acquisition, Algorithm development

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3-D optical fluorescent microscopy now becomes an efficient tool for the volume investigation of living biological samples. Developments in instrumentation have permitted to beat off the conventional Abbe limit. In any case the recorded image can be described by the convolution equation between the original object and the Point Spread Function (PSF) of the acquisition system. Due to the finite resolution of the instrument, the original object is recorded with distortions and blurring, and contaminated by noise. This induces that relevant biological information cannot be extracted directly from raw data stacks. If the goal is 3-D quantitative analysis, then to assess optimal performance of the instrument and to ensure the data acquisition reproducibility, the system characterization is mandatory. The PSF represents the properties of the image acquisition system; we have proposed the use of statistical tools and Zernike moments to describe a 3-D PSF system and to quantify the variation of the PSF. This first step toward standardization is helpful to define an acquisition protocol optimizing exploitation of the microscope depending on the studied biological sample. Before the extraction of geometrical information and/or intensities quantification, the data restoration is mandatory. Reduction of out-of-focus light is carried out computationally by deconvolution process. But other phenomena occur during acquisition, like fluorescence photo degradation named "bleaching", inducing an alteration of information needed for restoration. Therefore, we have developed a protocol to pre-process data before the application of deconvolution algorithms. A large number of deconvolution methods have been described and are now available in commercial package. One major difficulty to use this software is the introduction by the user of the "best" regularization parameters. We have pointed out that automating the choice of the regularization level; also greatly improves the reliability of the measurements although it facilitates the use. Furthermore, to increase the quality and the repeatability of quantitative measurements a pre-filtering of images improves the stability of deconvolution process. In the same way, the PSF prefiltering stabilizes the deconvolution process. We have shown that Zemike polynomials can be used to reconstruct experimental PSF, preserving system characteristics and removing the noise contained in the PSF.

Proceedings Article | 14 April 2006 Paper

Contribution of data pre-processing to deconvolution of 3-D fluorescence microscopy images

Arnaud De Meyer, Bruno Colicchio, Jan De Mey, Georges Jung, Alain Dieterlen, Olivier Haeberlé, Serge Jacquey

Proceedings Volume 6191, 61910G (2006) https://doi.org/10.1117/12.662953

KEYWORDS: Point spread functions, Deconvolution, Luminescence, Microscopy, 3D image processing, Data acquisition, 3D acquisition, Signal to noise ratio, Optical microscopy, Spindles

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Proceedings Article | 1 September 2004 Paper

Laser-induced optronic countermeasure against charge-coupled devices and optronic counter-countermeasure in the visible region and infrared region

Nicolas Hueber, Jean-Pierre Moeglin, Alain Dieterlen, Alain Boffy

Proceedings Volume 5417, (2004) https://doi.org/10.1117/12.537520

KEYWORDS: Charge-coupled devices, Cameras, Optoelectronics, Pulsed laser operation, Objectives, Diffusion, Staring arrays, Laser countermeasures, Silicon, Video

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Proceedings Article | 4 June 2004 Paper

Identification and restoration in 3D fluorescence microscopy

Alain Dieterlen, Chengqi Xu, Olivier Haeberle, Nicolas Hueber, R. Malfara, B. Colicchio, Serge Jacquey

Proceedings Volume 5477, (2004) https://doi.org/10.1117/12.559754

KEYWORDS: Point spread functions, Deconvolution, Microscopy, Luminescence, 3D acquisition, Image processing, 3D image processing, Microscopes, Optical microscopy, Image acquisition

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Proceedings Article | 2 November 2001 Paper

PSF identification applied to 3D fluorescence microscopy quantification

Alain Dieterlen, Marie-Pierre Gramain, Chengqi Xu, Francois Guillemin, Serge Jacquey

Proceedings Volume 4431, (2001) https://doi.org/10.1117/12.447403

KEYWORDS: Point spread functions, Luminescence, Deconvolution, Microscopy, 3D acquisition, 3D image processing, Monochromatic aberrations, Image processing, Data acquisition, Objectives

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Proceedings Article | 29 December 1997 Paper

Fluorescence microscopy image deconvolution: application to anthracycline distribution in breast cancer cells

Marie-Pierre Gramain, Corinne Bour, Alain Chomik, Alain Dieterlen, Olivier Haeberle, Jean Jacques Meyer, Sophie Marchal, Jean-Louis Merlin, Francois Guillemin

Proceedings Volume 3197, (1997) https://doi.org/10.1117/12.297960

KEYWORDS: Deconvolution, Luminescence, Microscopy, Point spread functions, Image segmentation, Cameras, Image processing algorithms and systems, Breast cancer, Image analysis, Lawrencium

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Showing 5 of 14 publications

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