Automated phase unwrapping in digital holography with deep learning

• Published in: Biomedical optics express 2021-11, Vol.12 (11), p.7064-7081
• Main Authors: Park, Seonghwan, Kim, Youhyun, Moon, Inkyu
• Format: Article
• Language: English
• ISSN: 2156-7085; 2156-7085
• DOI: 10.1364/BOE.440338

Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between −π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.