DHM

Compensation enhancement by the patch-based inpainting in off-axis digital holographic microscopy

a digital holographic phase reconstruction algorithm via the patch-based image inpainting method is proposed in this paper. The algorithm can eliminate system aberrations in the off-axis DHM, including the tilt and spherical aberration. By using a mask to cover the sample interference fringes and restoring the sample region with the single source patch-based synthesis algorithm, all the system aberrations are fitted and effectively compensated. The results in simulation and experiment reveal that the proposed method can achieve high compensation accuracy and robustness.

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.