Synchronous edge-enhanced and bright-field 3D imaging in single-shot FINCH enabled by deep learning

Edge-enhanced imaging and bright-field imaging reveal different morphological characteristics of an object. Hence, a system capable of realizing these modalities simultaneously is vitally essential for diverse applications. Here, we propose and demonstrate a method to combine deep learning (DL) with Fresnel incoherent correlation holography (FINCH) to achieve edge-enhanced and bright-field 3D imaging synchronously using only a single hologram. An integrated ResNet and U-net DL model is designed to predict the complex holograms with the spiral-FINCH and dual-lens FINCH from one input hologram, thereby obtaining the edge-enhanced and brightfield images at the three spatial dimensions through Fresnel propagation. Imaging experiments with different objects verify its capability to simultaneously perform multifunctional 3D imaging. This multifunctional 3D imaging system holds promising applications in biomedical imaging and defect detection, offering a novel tool for enhanced visualization and analysis.