Dr. Antonio Lorente-Mur Profile

Dr. Antonio Lorente-Mur

at INSA LYON Creatis

SPIE Involvement:

Author

Publications (3)

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

Read Abstract +

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 | 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

Read Abstract +

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

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years