dc.contributor.author | Rostan, Julen | |
dc.contributor.author | Incardona, Nicolo | |
dc.contributor.author | Sánchez-Ortiga, Emilio | |
dc.contributor.author | Martínez-Corral, Manuel | |
dc.contributor.author | Latorre Carmona, Pedro | |
dc.date.accessioned | 2023-01-26T13:35:57Z | |
dc.date.available | 2023-01-26T13:35:57Z | |
dc.date.issued | 2022-05 | |
dc.identifier.uri | http://hdl.handle.net/10259/7338 | |
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. | en |
dc.description.sponsorship | This research was funded by Grant RTI2018-099041-B-I00, which is co-founded by the Ministerio de Ciencia, Innovacion y Universidades (Spain), by the European Regional Development Fund, and by Generalitat Valenciana (Spain) under Grant PROMETEO/2019/048. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors. 2022, V. 22, n. 9, 3487 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Fourier lightfield microscopy | en |
dc.subject | View synthesis | en |
dc.subject | Neural radiance fields | en |
dc.subject | 3D microscopy | en |
dc.subject.other | Informática | es |
dc.subject.other | Computer science | en |
dc.title | Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy | en |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.publisherversion | https://doi.org/10.3390/s22093487 | es |
dc.identifier.doi | 10.3390/s22093487 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099041-B-I00/ES/MICROSCOPIO MULTIMODAL PARA LA OBTENCION DE IMAGENES BIOMEDICAS 3D/ | es |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F048/ | es |
dc.identifier.essn | 1424-8220 | |
dc.journal.title | Sensors | en |
dc.volume.number | 22 | es |
dc.issue.number | 9 | es |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |