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Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy

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Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy

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dc.contributor.author	Rostan, Julen
dc.contributor.author	Incardona, Nicolò
dc.contributor.author	Sánchez Ortiga, Emilio
dc.contributor.author	Martínez Corral, Manuel
dc.contributor.author	Latorre-Carmona, Pedro
dc.date.accessioned	2022-05-05T13:53:00Z
dc.date.available	2022-05-05T13:53:00Z
dc.date.issued	2022
dc.identifier.citation	Rostan, Julen Incardona, Nicolò Sánchez Ortiga, Emilio Martínez Corral, Manuel Latorre-Carmona, Pedro 2022 Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy Sensors 22 3487
dc.identifier.uri	https://hdl.handle.net/10550/82619
dc.description.abstract	Current interest in Fourier lightfield microscopy is increasing, due to its ability to acquire 3D images of thick dynamic samples. This technique is based on simultaneously capturing, in a single shot, and with a monocular setup, a number of orthographic perspective views of 3D microscopic samples. An essential feature of Fourier lightfield microscopy is that the number of acquired views is low, due to the trade-off relationship existing between the number of views and their corresponding lateral resolution. Therefore, it is important to have a tool for the generation of a high number of synthesized view images, without compromising their lateral resolution. In this context we investigate here the use of a neural radiance field view synthesis method, originally developed for its use with macroscopic scenes acquired with a moving (or an array of static) digital camera(s), for its application to the images acquired with a Fourier lightfield microscope. The results obtained and presented in this paper are analyzed in terms of lateral resolution and of continuous and realistic parallax. We show that, in terms of these requirements, the proposed technique works efficiently in the case of the epi-illumination microscopy mode.
dc.language.iso	eng
dc.relation.ispartof	Sensors, 2022, vol. 22, num. 3487
dc.subject	Microscòpia
dc.subject	Fourier, Anàlisi de
dc.title	Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy
dc.type	journal article	es_ES
dc.date.updated	2022-05-05T13:53:00Z
dc.identifier.doi	10.3390/s22093487
dc.identifier.idgrec	152417
dc.rights.accessRights	open access	es_ES