Deep learning

Quantitative phase imaging in digital holographic microscopy based on image inpainting using a two-stage generative adversarial network

The results of our experiment indicate the viability and accuracy of the presented method. Additionally, this work can pave the way for the evaluation of new applications of GAN in DHM.

Phase aberration compensation via deep learning in digital holographic microscopy

The experimental results confirm that the trained CNN can accurately segment the sample from the background area of the hologram, and that this method is applicable and effective in off-axis DHM.