UCLA engineers use deep learning to reconstruct holograms and improve optical microscopy

A form of machine learning called deep learning is one of the key technologies behind recent advances in applications like real-time speech recognition and automated image and video labeling. The approach, which uses multi-layered artificial neural networks to automate data analysis, also has shown significant promise for health care: It could be used, for example, to automatically identify abnormalities in patients' X-rays, CT scans and other medical images and data. In two new papers, UCLA researchers report that they have developed new uses for deep learning: reconstructing a hologram to form a microscopic image of an object and improving optical microscopy. Their new holographic imaging technique produces better images than current methods that use multiple holograms, and it's easier to implement because it requires fewer measurements and performs computations faster. The research was led by Aydogan Ozcan, an associate director of the UCLA California NanoSystems Institute and the Chancellor's Professor of Electrical and Computer Engineering at the UCLA Henry Samueli School of Engineering and Applied Science; and by postdoctoral scholar Yair Rivenson and graduate student Yibo Zhang, both of UCLA's electrical and computer engineering department.