3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging
Andrew H. Proppe Guillaume Thekkadath Duncan England Philip J. Bustard Frédéric Bouchard Jeff S. Lundeen Benjamin J. Sussman
In recent years, neural networks have been used to solve phase retrieval
problems in imaging with superior accuracy and speed than traditional
techniques, especially in the presence of noise. However, in the context of
interferometric imaging, phase noise has been largely unaddressed by existing
neural network architectures. Such noise arises naturally in an interferometer
due to mechanical instabilities or atmospheric turbulence, limiting measurement
acquisition times and posing a challenge in scenarios with limited light
intensity, such as remote sensing. Here, we introduce a 3D-2D Phase Retrieval
U-Net (PRUNe) that takes noisy and randomly phase-shifted interferograms as
inputs, and outputs a single 2D phase image. A 3D downsampling convolutional
encoder captures correlations within and between frames to produce a 2D latent
space, which is upsampled by a 2D decoder into a phase image. We test our model
against a state-of-the-art singular value decomposition algorithm and find
PRUNe reconstructions consistently show more accurate and smooth
reconstructions, with a x2.5 - 4 lower mean squared error at multiple
signal-to-noise ratios for interferograms with low (< 1 photon/pixel) and high
(~100 photons/pixel) signal intensity. Our model presents a faster and more
accurate approach to perform phase retrieval in extremely low light intensity
interferometry in presence of phase noise, and will find application in other
multi-frame noisy imaging techniques.