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REPAID: resolution-enhanced plenoptic all-in-focus imaging using deep neural networks

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Abstract

Due to limited depth-of-focus, classical 2D images inevitably lose details of targets out of depth-of-focus, while all-in-focus images break through the limit by fusing multi-focus images, thus being able to focus on targets in extended depth-of-view. However, conventional methods can hardly obtain dynamic all-in-focus imaging in both high spatial and temporal resolutions. To solve this problem, we design REPAID, meaning resolution-enhanced plenoptic all-in-focus imaging using deep neural networks. In REPAID, multi-focus images are first reconstructed from a single-shot plenoptic image, then upsampled using specially designed deep neural networks suitable for real scenes without ground truth to finally generate all-in-focus image in both high temporal and spatial resolutions. Experiments on both static and dynamic scenes have proved that REPAID can obtain high-quality all-in-focus imaging when using simple setups only; therefore, it is a promising tool in applications especially intended for imaging dynamic targets in large depth-of-view.

© 2021 Optical Society of America

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Supplementary Material (2)

NameDescription
Supplement 1       Supplemental Document
Visualization 1       Visualization 1

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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