Phase-aberration compensation via deep learning in digital holographic microscopy

Digital holographic microscopy (DHM), a quantitative phase-imaging technology, has been widely used in various applications. Phase-aberration compensation in off-axis DHM is vital to reconstruct topographical images with high precision, especially for microstructures with a small background or a dense phase distribution. We propose a numerical method based on deep learning combined with DHM. First, a convolutional neural network (CNN) recognizes and segments the sample and the background area of the hologram. Zernike polynomial fitting is then executed on the extracted background area. Finally, the whole process of phase-aberration compensation is automatically performed. To obtain a robust and accurate deep-learning model for hologram segmentation, we collected many holograms corresponding to several samples that had different morphological characteristics. 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.

Automated summary

A numerical method based on deep learning combined with DHM is proposed for phase-aberration compensation, achieving high precision in reconstructing topographical images. Experimental results confirm that the trained CNN can accurately segment the sample from the background area of the hologram, demonstrating the applicability and effectiveness of this method in off-axis DHM.