2026-05-12T08:08:56Zhttps://riubu.ubu.es/oai/request

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

Rostan, Julen 039971ee-2325-4eb1-a80e-b4c21707787a 600 Incardona, Nicolo 5ff5c20e-6796-44f2-9f17-9899524d4956 600 Sánchez-Ortiga, Emilio f5b59f74-08d9-4d32-9f3b-f61924ef3e6c 600 Martínez-Corral, Manuel b9475b87-52cf-4d36-927a-6d390d893960 600 Latorre Carmona, Pedro 815 500 0000-0001-6984-5173 2023-01-26T13:35:57Z 2023-01-26T13:35:57Z 2022-05 http://hdl.handle.net/10259/7338 10.3390/s22093487 1424-8220 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. application/pdf eng MDPI 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 Fourier lightfield microscopy View synthesis Neural radiance fields 3D microscopy Informática Computer science Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sensors 22 9 THUMBNAIL Rostan-sensors_2022.pdf.jpg Rostan-sensors_2022.pdf.jpg IM Thumbnail image/jpeg 3301 https://riubu.ubu.es/bitstream/10259/7338/4/Rostan-sensors_2022.pdf.jpg 7920669393ea1f6cae66c4e04612d2bd MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/7338/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/7338/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Rostan-sensors_2022.pdf Rostan-sensors_2022.pdf application/pdf 15322092 https://riubu.ubu.es/bitstream/10259/7338/1/Rostan-sensors_2022.pdf ec9d4f4403f5ad44f52849aa5fe06cbc MD5 1 10259/7338 oai:riubu.ubu.es:10259/7338 2023-03-21 11:30:10.287 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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