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

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. In this study, 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. This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram, requiring fewer measurements in addition to being computationally faster. We validated this method by reconstructing the phase and amplitude images of various samples, including blood and Pap smears and tissue sections. These results highlight that challenging problems in imaging science can be overcome through machine learning, providing new avenues to design powerful computational imaging systems. A new method that uses neural-network-based deep learning could lead to faster and more accurate holographic image reconstruction and phase recovery. Optoelectronic sensors such as charge-coupled devices and complementary metal-oxide–semiconductor imagers are sensitive to intensity but are unable to directly detect the phase of light waves diffracted from an object. Additional information or measurement is thus needed to recover the missing phase information, which enables reconstructing the image of the sample. Now, Aydogan Ozcan and colleagues from the University of California, Los Angeles in the USA have designed a neural network that can perform phase recovery and holographic image reconstruction from a single intensity-only hologram. Using deep learning, they demonstrated the elimination of twin-image and self-interference-related spatial artifacts arising from missing phase information. The technique could significantly simplify the imaging hardware and speed up the image acquisition and reconstruction processes in various holographic and coherent imaging systems.