Light-Field Microscopy with a Consumer Light-Field Camera

Mignard-Debise, Loïs; Ihrke, Ivo

doi:10.1109/3dv.2015.45

Cited by 15 publications

(12 citation statements)

References 20 publications

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“…The colloidal and bacterial suspensions are observed with light field microscope [30] (nikon Eclipse Ti) in bright field with an objective 10× of numerical aperture N.A. = 0.3, Fig.1.b-c.…”

Section: Microscope and Acquisition Parametersmentioning

confidence: 99%

“…Third, we radial average D( q, ∆t) and therefore drop the dependence on the orientation of the wavevector q. The contribution of the dark, shot and read-out noise [30,32] of the camera add some noise to each images and is taken into account by adding a supplementary term B(q). B(q) is decorrelated at all time and therefore independent of ∆t.…”

Section: B Ddm Principlementioning

confidence: 99%

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Differential dynamic microscopy to characterize Brownian motion and bacteria motility

Germain¹,

Leocmach²,

Gibaud³

2016

American Journal of Physics

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We have developed a lab work module where we teach undergraduate students how to quantify the dynamics of a suspension of microscopic particles, measuring and analyzing the motion of those particles at the individual level or as a group. Differential Dynamic Microscopy (DDM) is a relatively recent technique that precisely does that and constitutes an alternative method to more classical techniques such as dynamics light scattering (DLS) or video particle tracking (VPT). DDM consists in imaging a particle dispersion with a standard light microscope and a camera. The image analysis requires the students to code and relies on digital Fourier transform to obtain the intermediate scattering function, an autocorrelation function that characterizes the dynamics of the dispersion. We first illustrate DDM on the textbook case of colloids where we measure the diffusion coefficient. Then we show that DDM is a pertinent tool to characterize biologic systems such as motile bacteria i.e.bacteria that can self propel, where we not only determine the diffusion coefficient but also the velocity and the fraction of motile bacteria. Finally, so that our paper can be used as a tutorial to the DDM technique, we have joined to this article movies of the colloidal and bacterial suspensions and the DDM algorithm in both Matlab and Python to analyze the movies.

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Section: Microscope and Acquisition Parametersmentioning

confidence: 99%

Section: B Ddm Principlementioning

confidence: 99%

Differential dynamic microscopy to characterize Brownian motion and bacteria motility

Germain¹,

Leocmach²,

Gibaud³

2016

American Journal of Physics

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“…3.1, Levoy's system captures light fields with high angular resolution (17 × 17), but at reduced spatial resolution. Debise and Ihrke [60] developed a light field microscope using a consumer light field camera, Lytro, and an optical matching system, and achieved a maximum spatial resolution of about 6.25µm.…”

Section: Spatial Multiplexingmentioning

confidence: 99%

Light Field Image Processing: An Overview

Masiá

Jarabo

et al. 2017

IEEE J. Sel. Top. Signal Process.

491

215

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Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher-dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics and signal processing communities. In this article, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data. Index Terms-Light field imaging, light field processing.

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“…In general, this is not the case. 1,3 Real light field systems exhibit vignetting and Mignard-Debise and colleague 3 introduced a separation into spatial and angular vignetting, as illustrated in Fig. 2.…”

Section: Vignetting In Real Light Field Systems and The Equivalent F-mentioning

confidence: 99%

An equivalent F-number for light field systems: light efficiency, signal-to-noise ratio, and depth of field

Ihrke

2019

Fourteenth International Conference on Quality Control by Artificial Vision

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The paper discusses the light efficiency and SNR of light field imaging systems in comparison to classical 2D imaging. In order to achieve this goal, I define the "equivalent f-number" as a concept of capturing the light gathering ability of the light field system. Since the f-number, in classical imaging systems, is conceptually also linked with the depth-of-field, I discuss an appropriate depth-of-field interpretation for light field systems.Keywords: light field imaging, light efficiency, f-number, equivalent f-number ¶ This can be seen by considering an equivalent light field system (LF) to the high-resolution standard system (St): Keeping the field-of-view and the image-side pixel size constant, requires an equivalent focal length of f /N .

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Light-Field Microscopy with a Consumer Light-Field Camera

Cited by 15 publications

References 20 publications

Differential dynamic microscopy to characterize Brownian motion and bacteria motility

Differential dynamic microscopy to characterize Brownian motion and bacteria motility

Light Field Image Processing: An Overview

An equivalent F-number for light field systems: light efficiency, signal-to-noise ratio, and depth of field

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