Deep ghost phase imaging
Koshi Komuro, Takanori Nomura, George Barbastathis
Abstract
Deep-learning-based single-pixel phase imaging is proposed. The method, termed deep ghost phase imaging (DGPI), succeeds the advantages of computational ghost imaging, i.e., has the phase imaging quality with high signal-to-noise ratio derived from the Fellgett's multiplex advantage and the point-like detection of diffracted light from objects. A deep convolutional neural network is learned to output a desired phase distribution from an input of a defocused intensity distribution reconstructed by the single-pixel imaging theory. Compared to the conventional interferometric and transport-of-intensity approaches to single-pixel phase imaging, the DGPI requires neither additional intensity measurements nor explicit approximations. The effects of defocus distance and light level are investigated by numerical simulation and an optical experiment confirms the feasibility of the DGPI.