Abstract
We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.
Highlights
Recent advances in quantitative phase imaging (QPI) offer an extended opportunity for the label-free, non-destructive, and quantitative study of biological specimens [1]
We have proposed and experimentally validated a deep learning algorithm that suppresses the coherent noise in refractive index tomograms
We demonstrated its quantitative denoising performance and generalization capability through various biological experiments
Summary
Recent advances in quantitative phase imaging (QPI) offer an extended opportunity for the label-free, non-destructive, and quantitative study of biological specimens [1]. As a scheme for three-dimensional (3D) QPI, optical diffraction tomography (ODT) is an imaging method that uses angularly varying illumination to reconstruct the 3D refractive index (RI) distribution of a microscopic sample. Unwanted interference of the coherent light generates this noise in the form of fringe patterns and speckle grains [14]. This is mainly caused by multiple reflection from optical elements and dust particles. Misalignment of the optical system could deteriorate the reconstructed tomogram. We term this category of noise as “coherent noise” throughout this paper
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