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.
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