Abstract
This paper proposes a novel approach for high-resolution light field microscopy imaging by using a camera array. In this approach, we apply a two-stage relay system for expanding the aperture plane of the microscope into the size of an imaging lens array, and utilize a sensor array for acquiring different sub-apertures images formed by corresponding imaging lenses. By combining the rectified and synchronized images from 5 × 5 viewpoints with our prototype system, we successfully recovered color light field videos for various fast-moving microscopic specimens with a spatial resolution of 0.79 megapixels at 30 frames per second, corresponding to an unprecedented data throughput of 562.5 MB/s for light field microscopy. We also demonstrated the use of the reported platform for different applications, including post-capture refocusing, phase reconstruction, 3D imaging, and optical metrology.
Highlights
Light field microscopy (LFM) [1] is a scanless 3D computational imaging approach that records both 2D spatial and 2D angular distribution of light passing through a specimen
To the best of our knowledge, we are the first to apply a camera array for light field imaging in microscopy, which can achieve high-resolution and high frame rate light field videos acquisition with high accuracy
We have successfully demonstrated the acquisition of color light field videos for various fast-moving microscopic specimens with a spatial resolution of 0.79 megapixels, angular views of 25, and a temporal resolution of 33 ms (30 fps)
Summary
Light field microscopy (LFM) [1] is a scanless 3D computational imaging approach that records both 2D spatial and 2D angular distribution of light passing through a specimen. By incorporating prior knowledge about the object, for example, Gaussian angular distribution assumption for light field moment imaging [10, 11], Lambertian reflectance priors with super-resolution [12,13,14] or learning an over-complete dictionary to exploit its intrinsic redundancy [15, 16], high-resolution light fields can be computationally reconstructed. These empirical assumptions do not always hold for microscopic samples
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
