Denoising holograms using unsupervised learning (Conference Presentation)

Keren Ben-Yehuda; Natan T. Shaked

doi:10.1117/12.2559650

22 April 2020 Denoising holograms using unsupervised learning (Conference Presentation)

Keren Ben-Yehuda, Natan T. Shaked

Proceedings Volume 11402, Three-Dimensional Imaging, Visualization, and Display 2020; 114020I (2020) https://doi.org/10.1117/12.2559650
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

We present a new tool based on unsupervised learning to ensure the pictorial information recorded is as close as possible to the original by denoising the images produced, and thereby allowing to make more knowledgeable decisions. The algorithm is used to clean off-axis holograms. To denoise the acquired off-axis holograms, our technique takes advantage of the prior knowledge we have regarding the expected image and uses it to erase the noise, providing a significantly clearer image. We applied the technique to off-axis holograms of individual sperm cells acquired without labeling.

Conference Presentation

Citation Download Citation

Keren Ben-Yehuda and Natan T. Shaked "Denoising holograms using unsupervised learning (Conference Presentation)", Proc. SPIE 11402, Three-Dimensional Imaging, Visualization, and Display 2020, 114020I (22 April 2020); https://doi.org/10.1117/12.2559650

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