High Bandwidth-Utilization Digital Holographic Reconstruction Using an Untrained Neural Network

نویسندگان

چکیده

Slightly off-axis digital holographic microscopy (DHM) is the extension of holography imaging technology toward high-throughput modern optical technology. However, it difficult for method based on conventional linear Fourier domain filtering to solve artifacts caused by spectral aliasing problem. In this article, we propose a novel high-accuracy, artifacts-free, single-frame, phase demodulation scheme low-carrier-frequency holograms, which incorporates physical model into deep neural network (DNN) without training beforehand massive dataset. Although end-to-end learning (DL) can achieve high-accuracy recovery directly from single-frame hologram, datasets and ground truth collection be prohibitively laborious time-consuming. Our recognizes such low-carrier frequency fringe process as nonlinear optimization problem, reconstruct artifact-free details gradually hologram. The resolution target simulation experiment results quantitatively demonstrate that proposed possesses better artifact suppression high-resolution capabilities than methods. addition, live-cell also indicates practicality technique in biological research.

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2022

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app122010656