Quantum mechanics

Phase (matter)

Psychoanalysis

Microscope

Microscopy

Holography

Image processing

Digital imaging

Phase retrieval

Computer science

Physics

Digital holography

Psychology

Optics

Image (mathematics)

Focus (optics)

Artificial intelligence

Digital image

Deep learning

Artificial neural network

Digital image processing

Digital holographic microscopy

Compensation (psychology)

Convolutional neural network

Fourier transform

Computer vision

Optics Express

Traditional digital holographic imaging algorithms need multiple iterations to obtain focused reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the problem of phase compensation in addition to focusing task. Here, a new method is proposed for fast digital focus, where we use U-type convolutional neural network (U-net) to recover the original phase of microscopic samples. Generated data sets are used to simulate different degrees of defocused image, and verify that the U-net can restore the original phase to a great extent and realize phase compensation at the same time. We apply this method in the construction of real-time off-axis digital holographic microscope and obtain great breakthroughs in imaging speed.

Fast phase retrieval in off-axis digital holographic microscopy through deep learning