2026-07-28T23:51:49Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73382023-03-21T10:30:10Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy Rostan, Julen Incardona, Nicolo Sánchez-Ortiga, Emilio Martínez-Corral, Manuel Latorre Carmona, Pedro Fourier lightfield microscopy View synthesis Neural radiance fields 3D microscopy Informática Computer science 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. 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. 2023-01-26T13:35:57Z 2023-01-26T13:35:57Z 2022-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/10259/7338 10.3390/s22093487 1424-8220 eng Sensors. 2022, V. 22, n. 9, 3487 https://doi.org/10.3390/s22093487 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/ info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F048/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf MDPI https://riubu.ubu.es/bitstream/10259/7338/4/Rostan-sensors_2022.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by/4.0/ RIUBU. Repositorio Institucional de la Universidad de Burgos http://hdl.handle.net/10259/7338