Digital holographic microscopy (DHM) provides numerous advantages, such as noninvasive sample analysis, real-time dynamic detection, and three-dimensional (3D) reconstruction, making it a valuable tool in fields such as biomedical research, cell mechanics, and environmental monitoring. To achieve more accurate and comprehensive imaging, it is crucial to capture detailed information on the microstructure and 3D morphology of samples. Phase processing of holograms is essential for recovering phase information, thus making it a core component of DHM. Traditional phase processing techniques often face challenges, such as low accuracy, limited robustness, and poor generalization. Recently, with the ongoing advancements in deep learning, addressing phase processing challenges in DHM has become a key research focus. This paper provides an overview of the principles behind DHM and the characteristics of each phase processing step. It offers a thorough analysis of the progress and challenges of deep learning methods in areas such as phase retrieval, filtering, phase unwrapping, and distortion compensation. The paper concludes by exploring trends, such as ultrafast 3D holographic reconstruction, high-throughput holographic data analysis, multimodal data fusion, and precise quantitative phase analysis.
Jiang et al. (Wed,) studied this question.