Dr. Nicolas Ducros Profile

Dr. Nicolas Ducros

Associate Professor at INSA Lyon

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Publications (17)

Proceedings Article | 24 July 2024 Presentation

Reproducible hyperspectral single-pixel imaging using the OpenSpyrit ecosystem

Juan Felipe Perez Juste Abascal, Laurent Mahieu-Williame, Thomas Baudier, Nicolas Ducros

Proceedings Volume PC12996, PC129960C (2024) https://doi.org/10.1117/12.3021886

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Proceedings Article | 18 June 2024 Presentation

Hyperspectral computational SPIM for quantitative multicolor imaging

Cédric Ray, Sébastien Crombez, Chloé Exbrayat-Heritier, Florence Ruggiero, Nicolas Ducros

Proceedings Volume PC12996, PC129960Q (2024) https://doi.org/10.1117/12.3022171

KEYWORDS: Biological samples, Hyperspectral imaging, Biological imaging, Structured light, Fluorophores, Fluorescence, Voxels, Stereoscopy, Spectroscopy, Spectroscopes

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Proceedings Article | 30 May 2022 Presentation

A fast computational approach for high spectral resolution imaging

Laurent Mahieu-Williame, Antonio Lorente-Mur, Valeriya Pronina, Bruno Montcel, Françoise Peyrin, Nicolas Ducros

Proceedings Volume PC12136, PC121360F (2022) https://doi.org/10.1117/12.2620695

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Spectral imaging is at the cornerstone of many fields, including astronomy, environmental monitoring, food processing, agriculture, and biomedical imaging. While most current spectral techniques lack imaging speed, we describe a computational approach that allows for fast high spectral resolution imaging. Our set-up maps the image of the scene into the fiber of a compact spectrometer through a digital micromirror device (DMD), where a series of multiplexing patterns are uploaded. After a reconstruction step, the hypercube of the scene can be recovered. DMDs represent the fastest technology (e.g., >20 kHz) for spatial light modulators. The raw data is acquired for a (x,y,λ) hypercube of size 64x64x2048 in about 12 s, while the Hadamard inversion takes <1 s. The acquisition speed can be further reduced by limiting the number of Hadamard patterns; however, the resulting imaging reconstruction problem is turned into an underdetermined inverse problem, which requires regularization techniques to be used to obtain acceptable solutions, in particular, in the presence of noise. Deep learning is a very efficient framework to solve inverse problems in imaging. Following a recent trend, several convolution neural network architectures have provided a link between deep and optimization-based image reconstruction methods. Contrary to the initially proposed “black box” networks, these deep-learning methods rely on a forward operator and lead to more interpretable networks. Here, we review deep architectures for single-pixel image reconstruction and show that the network can be trained easily, in a end-to-end manner, using databases such as STL-10 or ImageNet. We present reconstruction results from simulated and experimental single-pixel acquisitions. We show that EM-Net generalizes very well to noise levels that are unseen during the training, despite having fewer learned parameters than alternative methods. The proposed EM-Net generally reconstructs images with fewer artifacts and with higher signal-to-noise ratios, particularly in high-noise situations.

Proceedings Article | 30 May 2022 Presentation

Hyperspectral compressive microscopy based on structured light sheet and deep convolutionnal neural network

Sébastien Crombez, Chloé Exbrayat-Heritier, Florence Ruggiero, Cédric Ray, Nicolas Ducros

Proceedings Volume PC12136, PC121360E (2022) https://doi.org/10.1117/12.2616521

KEYWORDS: Structured light, Microscopy, Neural networks, Denoising, Spectral resolution, Reconstruction algorithms, Hyperspectral imaging, Spectroscopy, Spectroscopes, Optical imaging

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Proceedings Article | 15 April 2020 Presentation + Paper

Deep neural networks for single-pixel compressive video reconstruction

Antonio Lorente Mur, Bruno Montcel, Françoise Peyrin, Nicolas Ducros

Proceedings Volume 11351, 113510S (2020) https://doi.org/10.1117/12.2553326

KEYWORDS: Video, Neural networks, Skin, Surface plasmons, Veins, Video compression, Cameras, Digital micromirror devices, Image compression, Databases

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SPIE Journal Paper | 30 April 2019 Open Access

Single snapshot imaging of optical properties using a single-pixel camera: a simulation study

Enagnon Aguénounon, Foudil Dadouche, Wilfried Uhring, Nicolas Ducros, Sylvain Gioux

JBO, Vol. 24, Issue 07, 071612, (April 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.7.071612

KEYWORDS: Spatial frequencies, Optical properties, Image compression, Cameras, Optical imaging, Scattering, Absorption, Digital micromirror devices, Device simulation, Wavelets

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Proceedings Article | 7 March 2019 Presentation + Paper

Handling negative patterns for fast single-pixel lifetime imaging

Antonio Lorente Mur, Marien Ochoa, Jérémy Cohen, Xavier Intes, Nicolas Ducros

Proceedings Volume 10862, 108620A (2019) https://doi.org/10.1117/12.2511123

KEYWORDS: Spatial light modulators, Matrices, Image compression, Wavelets, Cameras, Sensors, Reconstruction algorithms, Time metrology, Hyperspectral imaging

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Proceedings Article | 4 October 2017 Paper

Sparse reconstruction methods in x-ray CT

J. F. P. J. Abascal, M. Abella, C. Mory, N. Ducros, C. de Molina, E. Marinetto, F. Peyrin, M. Desco

Proceedings Volume 10391, 1039112 (2017) https://doi.org/10.1117/12.2272711

KEYWORDS: Image restoration, Image quality, X-ray computed tomography, Reconstruction algorithms, Computed tomography, X-rays, Tissues, Motion estimation, Scanners, Compressed sensing, Inverse problems, Spectral computed tomography, Inverse imaging problems, Inverse problem on medical image, 3D image reconstruction

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Proceedings Article | 25 September 2017 Paper

A Kullback-Leibler approach for 3D reconstruction of spectral CT data corrupted by Poisson noise

Tom Hohweiller, Nicolas Ducros, Françoise Peyrin, Bruno Sixou

Proceedings Volume 10391, 103911E (2017) https://doi.org/10.1117/12.2273649

KEYWORDS: Signal to noise ratio, Bone, Tomography, Sensors, Tissues, 3D modeling, Computed tomography, MATLAB, Inverse problems, Signal attenuation

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Proceedings Article | 28 July 2017 Paper

Multiple-view time-resolved diffuse optical tomography based on structured illumination and compressive detection

Andrea Farina, Marta Betcke, Nicolas Ducros, Laura di Sieno, Andrea Bassi, Antonio Pifferi, Gianluca Valentini, Simon Arridge, Cosimo D'Andrea

Proceedings Volume 10412, 1041203 (2017) https://doi.org/10.1117/12.2290088

KEYWORDS: Diffuse optical tomography, Tomography, Scattering

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Proceedings Article | 30 March 2017 Paper

Diffuse optical tomography based on time-resolved compressive sensing

A. Farina, M. Betcke, L. Di Sieno, A. Bassi, N. Ducros, A. Pifferi, G. Valentini, S. Arridge, C. D'Andrea

Proceedings Volume 10059, 100590I (2017) https://doi.org/10.1117/12.2252594

KEYWORDS: Compressed sensing, Imaging systems, Diffuse optical tomography, Cameras, Scattering, Tissue optics, Tomography, Light scattering, Digital micromirror devices, Absorption

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Proceedings Article | 9 March 2017 Paper

A BVMF-B algorithm for nonconvex nonlinear regularized decomposition of spectral x-ray projection images

Mai Quyen Pham, Nicolas Ducros, Barbara Nicolas

Proceedings Volume 10132, 101323K (2017) https://doi.org/10.1117/12.2255656

KEYWORDS: Bone, Gadolinium, Tissues, Photon counting, Reconstruction algorithms, Detection and tracking algorithms, Computed tomography, Tomography, Inverse problems, Sensors, Computer simulations, X-ray imaging, X-rays

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Proceedings Article | 24 February 2017 Paper

Time-resolved wavelet-based acquisitions using a single-pixel camera

Florian Rousset, Nicolas Ducros, Andrea Farina, Gianluca Valentini, Cosimo D'Andrea, Françoise Peyrin

Proceedings Volume 10070, 1007016 (2017) https://doi.org/10.1117/12.2252436

KEYWORDS: Fluorescence lifetime imaging, Microscopy, Cameras, Imaging systems, Temporal resolution, Compressed sensing, Image quality, Image compression, Computing systems, Wavelets, Image restoration, Digital micromirror devices, Sensors, Image acquisition

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Proceedings Article | 18 March 2015 Paper

Diffuse optical tomography by using time-resolved single pixel camera

A. Farina, M. Lepore, L. Di Sieno, Alberto Dalla Mora, N. Ducros, A. Pifferi, G. Valentini, Simon Arridge, C. D'Andrea

Proceedings Volume 9319, 93191K (2015) https://doi.org/10.1117/12.2079650

KEYWORDS: Cameras, Diffuse optical tomography, Digital micromirror devices, Luminescence, Data acquisition, Charge-coupled devices, Optical tomography, Scattering, Sensors, Structured light

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Proceedings Article | 25 March 2013 Paper

Fluorescence molecular tomography on animal model by means of multiple views structured light illumination

N. Ducros, A. Bassi, G. Valentini, G. Canti, S. Arridge, C. D'Andrea

Proceedings Volume 8578, 85781X (2013) https://doi.org/10.1117/12.2004469

KEYWORDS: Luminescence, Tomography, Fluorescence tomography, Animal model studies, Charge-coupled devices, Image compression, Digital micromirror devices, 3D modeling, Light sources, Structured light

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SPIE Journal Paper | 23 January 2013 Open Access

Fluorescence molecular tomography of an animal model using structured light rotating view acquisition

Nicolas Ducros, Andrea Bassi, Gianluca Valentini, Gianfranco Canti, Simon Arridge, Cosimo D'Andrea

JBO, Vol. 18, Issue 02, 020503, (January 2013) https://doi.org/10.1117/12.10.1117/1.JBO.18.2.020503

KEYWORDS: Luminescence, Animal model studies, Tomography, Fluorescence tomography, Image compression, Data acquisition, Spatial resolution, Tissues, 3D modeling, Structured light

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Proceedings Article | 1 March 2011 Paper

Novel approaches based on structured light for fast diffuse optical tomography

Andrea Bassi, Nicolas Ducros, Cosimo D'Andrea, Gianluca Valentini, Simon Arridge

Proceedings Volume 7896, 78961T (2011) https://doi.org/10.1117/12.874812

KEYWORDS: Wavelets, Luminescence, Diffuse optical tomography, Image compression, Absorption, Optical tomography, Tomography, In vivo imaging, 3D modeling, Structured light

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Showing 5 of 17 publications

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