Advanced deep digital holographic microscopy

Accurate 3D positioning are essential in advanced fields such as computer vision, robotics, and microscopy, particularly for industrial and clinical applications. This presentation highlights recent developments in hybrid imaging techniques for 3D motion analysis and quantitative phase imaging in small-scale mechatronics and automated microscopy. By integrating digital holographic microscopy with deep learning models—including CNNs, Vision Transformers, and the GedankenNet architectures—this work enhances micro-vision machine capabilities. High-tech domains like micro-robotics and photonics demand precise, robust, and fast in-situ measurements using complex, multi-scale parameters. Unlike conventional data-driven methods, physics-based deep learning can be untrained or require minimal data, enabling optical computation and real-time adaptability. A prototype deep digital holographic microscope featuring a hybrid neural network for autofocusing and dynamic phase imaging will be introduced. The key challenge remains achieving nanometer-scale accuracy while ensuring reliability and interpretability in environments where hallucinated outputs from standard AI models are unacceptable.