Unpaired learning for digital holographic reconstruction and generation

Traditional numerical reconstruction methods in digital holography are faced with problems such as inaccurate and time-consuming unwrapping or the need to capture multiple holograms with different diffraction distances. In recent years, deep learning, as a new and effective optimization tool, has been widely used in digital holography. However, most supervised deep learning methods require large-scale paired data, and their preparation is time-consuming and laborious. Here, we propose a new deep learning approach that can use less unpaired data to train neural networks, thereby reducing the need for labeled data. This method can reconstruct complex amplitudes for holographic reconstruction and generate synthetic holograms at the same time. The reconstructed complex amplitudes have higher image quality, while the generated holograms can reconstruct the complex amplitudes successfully