  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.
