This article describes a dataset of synthetic images representing biological scenery as captured by a Fourier Lightfield Microscope (FLMic). It includes 22,416 images related to eight scenes composed of 3D models of objects typical for biological samples, such as red blood cells and bacteria, and categorized into Cells and Filaments groups. For each scene, two types of image data structures are provided: 51 × 51 Elemental Images (EIs) representing Densely Sampled Light Fields (DSLF) and 201 images composing Z-Scans of the scenes. Auxiliary data also includes information about camera intrinsic and extrinsic calibration parameters, object descriptions, and MATLAB scripts for camera pose compensation. The images have been generated using Blender. The dataset can be used to develop and assess methods for volumetric reconstruction from Light Field (LF) images captured by a FLMic.
Keywords: Blender; Cells; DCR, Dynamic Cutting Region; DSFL, Densely Sampled Light Field; EI, Elemental Image; FLMic, Fourier Lightfield Microscope; FiMic, Fourier Integral Microscope; Filaments; Fourier lightfield microscopy; GT, Ground Truth; LF, Light Field; Light field; NA, Numerical Apperture; RBC, Red Blood Cell; RoI, Region of Interest; Z-scan; Z-stack.
© 2022 The Authors. Published by Elsevier Inc.