HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model
Keyvan Jaferzadeh, Thomas Fevens
Abstract
Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram, which requires the precise setting of the reconstruction parameters, including reconstruction distance, a proper phase unwrapping algorithm, and component of wave vectors. This paper shows that deep learning can perform the complex light propagation task independent of the reconstruction parameters. We also show that the super-imposed twin-image elimination technique is not required to retrieve the quantitative phase image. The hologram at the single-cell level is fed into a trained image generator (part of a conditional generative adversarial network model), which produces the phase image. Also, the model's generalization is demonstrated by training it with holograms of size 512×512 pixels, and the resulting quantitative analysis is shown.