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DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning

Biology

DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning

Y. Zhang, B. Xiong, et al.

Discover DiLFM, a groundbreaking light field microscopy technique developed by Yuanlong Zhang, Bo Xiong, Yi Zhang, Zhi Lu, Jiamin Wu, and Qionghai Dai. This innovation leverages dictionary learning to tackle noise and artifacts in imaging, enhancing performance in low-light conditions and expanding its applications to high-speed blood cell counting and whole-brain calcium recording.

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Playback language: English
Abstract
Light field microscopy (LFM) suffers from artifacts and noise issues. This paper introduces DiLFM, a new LFM technique using dictionary learning to suppress artifacts and improve noise robustness. DiLFM demonstrates artifact-suppressed reconstructions in scattering samples and robust performance in low-light conditions, enabling applications like high-speed blood cell counting and whole-brain calcium recording.
Publisher
Light: Science & Applications
Published On
Oct 26, 2021
Authors
Yuanlong Zhang, Bo Xiong, Yi Zhang, Zhi Lu, Jiamin Wu, Qionghai Dai
Tags
light field microscopy
artifact suppression
noise reduction
dictionary learning
low-light imaging
biomedical applications
calcium recording

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