Translator Disclaimer
Presentation
20 August 2020 3D reconstruction of a hologram with brightfield contrast using deep learning
Author Affiliations +
Abstract
Holographic microscopy encodes the 3D information of a sample into a single hologram. However, holographic images are in general inferior to bright-field microscopy images in terms of contrast and signal-to-noise ratio, due to twin-image artifacts, speckle and out-of-plane interference. The contrast and noise problem of holography can be mitigated using iterative algorithms, but at the cost of additional measurements and time. Here, we present a deep-learning-based cross-modality imaging method to reconstruct a single hologram into volumetric images of a sample with bright-field contrast and SNR, merging the snapshot 3D imaging capability of holography with the image quality of bright-field microscopy.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yichen Wu, Yilin Luo, Gunvant Chaudhari, Yair Rivenson, Kevin De Haan, Ayfer Calis, and Aydogan Ozcan "3D reconstruction of a hologram with brightfield contrast using deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146919 (20 August 2020); https://doi.org/10.1117/12.2567300
PROCEEDINGS
PRESENTATION


SHARE
Advertisement
Advertisement
Back to Top