Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution

Autor: Alice Fontbonne, Pauline Trouvé-Peloux, Frédéric Champagnat, Gabriel Jobert, Guillaume Druart

Publikováno v: Sensors, Vol 23, Iss 23, p 9462 (2023)

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a

Externí odkaz: https://doaj.org/article/7078eaee42c74bdebb55fa7b871e8014

Single image depth-from-defocus with a learned covariance: algorithm and performance model for co-design

Autor: Benjamin Buat, Pauline Trouvé-Peloux, Frédéric Champagnat, Guy Le Besnerais

Publikováno v: Unconventional Optical Imaging III.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1ab650e2cd664251feeb74dcd27d96ba
https://doi.org/10.1117/12.2621252

Lagrangian Particle Tracking: a link between localization error and fraction of missed particles

Autor: Philippe Cornic, Benjamin Leclaire, Frédéric Champagnat

Publikováno v: 14th International Symposium on Particle Image Velocimetry. 1

This paper aims at analysing the behaviour of particle localisation error in 3D Lagrangian Particle Tracking (LPT) techniques, with a particular emphasis on general properties, independent of a specific algorithm. Based on the hypothesis that in LPT

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::004b7bea2aa7216c58f7114b1e7ab3be
https://doi.org/10.18409/ispiv.v1i1.186

Pix2Point : learning outdoor 3D using sparse point clouds and optimal transport

Autor: B. Le Saux, Marcela Carvalho, Frédéric Champagnat, Rémy Leroy, Pauline Trouvé-Peloux

Publikováno v: MVA 2021
MVA 2021, Jul 2021, VIRTUEL, Japan
HAL
MVA

Good quality reconstruction and comprehension of a scene rely on 3D estimation methods. The 3D information was usually obtained from images by stereo-photogrammetry, but deep learning has recently provided us with excellent results for monocular dept

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bd4d9fff49b92ab9ef7788f2f56fdac
https://hal.archives-ouvertes.fr/hal-03365994/document

Performance model of depth from defocus with an unconventional camera

Autor: Guillaume Druart, G. Le Besnerais, Jérôme Idier, Pauline Trouvé-Peloux, Frédéric Champagnat

Publikováno v: Journal of the Optical Society of America. A Optics, Image Science, and Vision
Journal of the Optical Society of America. A Optics, Image Science, and Vision, Optical Society of America, 2021, 38 (10), pp.1489. ⟨10.1364/josaa.424621⟩
Journal of the Optical Society of America. A Optics, Image Science, and Vision, 2021, 38 (10), pp.1489. ⟨10.1364/josaa.424621⟩

International audience; In this paper we present a generic performance model able to evaluate the accuracy of depth estimation using depth from defocus. This model only requires the sensor PSF at a given depth to evaluate the theoretical accuracy of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8957732221eb51a4d7b47390fb17ab0f
https://hal.archives-ouvertes.fr/hal-03378595