Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy.

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 m

SUBMITTER: Rostan J 

PROVIDER: S-EPMC9099650 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature