Single-shot higher-order transport-of-intensity quantitative phase imaging using deep learning.

Abstract

Single-shot higher-order transport-of-intensity quantitative phase imaging (SHOT-QPI) is proposed to realize simple, in-line, scanless, and single-shot QPI. However, the light-use efficiency of SHOT-QPI is low because of the use of an amplitude-type computer-generated hologram (CGH). Although a phase-type CGH overcomes the problem, the accuracy of the measured phase is degraded owing to distortion of the defocused intensity distributions, which is caused by a quantization error of the CGH. Alternative SHOT-QPI with the help of deep learning, termed Deep-SHOT, is proposed to solve a nonlinear problem between the distorted intensities and the phase. In Deep-SHOT, a neural network learns the relationship between a series of distorted intensity distributions and the ground truth phase distribution. Because the distortion of intensity distributions is intrinsic to an optical system, the neural network is optimized for the system, and the proposed method improves the accuracy of the measured phase. The results of a proof-of-principle experiment indicate that the use of multiple defocused intensities also improves accuracy, even the nonlinear problem.