Deep learning assisted variational Hilbert quantitative phase imaging

We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects

A recent article in the Opto-Electronic Advances(OEA)journal from Prof.Qian Chen and Prof.Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry(FPP).In this News&Views article,we explore potential avenues for future advancements,including expanding the measurement range through an extended number-theoretical approach,enhancing quality through the incorporation of horizontal fringes,and integrating data from other modalities to broaden the system's applications.