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Sensors 2019, 19(3), 500;

Robust Depth Estimation for Light Field Microscopy

Department of Computer Science, Christian-Albrecht-University, 24118 Kiel, Germany
Department of Optics, University of Valencia, E-46100 Burjassot, Spain
Author to whom correspondence should be addressed.
Received: 18 December 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 25 January 2019
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization. View Full-Text
Keywords: depth estimation; light field; microscope; stereo matching; defocus depth estimation; light field; microscope; stereo matching; defocus

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Palmieri, L.; Scrofani, G.; Incardona, N.; Saavedra, G.; Martínez-Corral, M.; Koch, R. Robust Depth Estimation for Light Field Microscopy. Sensors 2019, 19, 500.

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