Multi-directional sub-aperture wrapped phase aberration compensation for synthetic aperture digital holographic microscopy using deep learning

Abstract

The sensitivity of quantitative phase to subtle changes in the optical field makes synthetic aperture (SA) and phase retrieval affected by optical aberrations, limiting the imaging signal-to-noise ratio, reconstructed image quality and spatial resolution of synthetic aperture digital holographic microscopy (SA-DHM). In this paper, the constructed SA-DHM uses Digital Micromirror Device (DMD) to form the multi-directional oblique illumination (OI) and obtain a set of complementary apertures containing different spatial frequency information. A multi-directional sub-aperture wrapped phase aberration compensation method based on Moga-enhanced ConvNeXt architecture is proposed, and multiple multi-order gated aggregation blocks are integrated to directly construct the mapping relationship between the sub-aperture wrapped phase maps and the Zernike polynomial coefficients. A hybrid simulation dataset covering various types of micro/nano samples and phase aberrations is created, and a data augmentation method based on random linear combinations is introduced to enhance sample diversity and network generalization capability. Simulation and experimental results show that the proposed method achieves rapid and accurate aberration compensation and baseline unification of each sub-aperture prior to SA processing, enabling high-quality sub-aperture spectrum stitching and 1.87× super-resolution phase imaging, while eliminating the need for carrier frequency estimation, exact system parameters, or residual aberration correction.