Phase recovery and holographic image reconstruction using deep learning in neural networks

Understand

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography.

• Here we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training.
• This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference related spatial artifacts.
• Compared to existing approaches, this neural network based method is significantly faster to compute, and reconstructs improved phase and amplitude images of the objects using only one hologram, i.e., requires less number of measurements in addition to being computationally faster.