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Follow on Google News | Superior phase recovery and hologram reconstruction using a deep neural networkBy: UCLA ITA UCLA researchers have recently created a novel neural network architecture, termed Fourier Imager Network (FIN), which demonstrated unprecedented generalization to unseen sample types, also achieving superior computational speed in phase retrieval and holographic image reconstruction tasks. In this new approach, they introduced spatial Fourier transform modules that enable the neural network to take advantage of the spatial frequencies of the whole image. UCLA researchers trained their FIN model on human lung tissue samples and demonstrated its superior generalization by reconstructing the holograms of human prostate and salivary gland tissue sections, and Pap smear samples, which were never seen in the training phase. Published in Light: Science & Applications, a journal of Springer Nature, this new deep learning-based framework is reported to achieve higher image reconstruction accuracy compared to the classical hologram reconstruction algorithms and the state-of-the- This research was led by Dr. Aydogan Ozcan, Chancellor's Professor and Volgenau Chair for Engineering Innovation at UCLA and HHMI Professor with the Howard Hughes Medical Institute. The other authors of this work include Hanlong Chen, Luzhe Huang, and Tairan Liu, all from the Electrical and Computer Engineering department at UCLA. Prof. Ozcan also has UCLA faculty appointments in the bioengineering and surgery departments and is an associate director of the California NanoSystems Institute. See article: https://www.nature.com/ End
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