3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging

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, …

accuracy architectures context cs.cv cs.lg eess.iv imaging network networks neural nets neural network neural networks noise physics.optics retrieval solve speed