The researchers shot images of lung tissue samples, blood and Pap smears, first using a standard laboratory-grade microscope, and then with a smartphone with the 3D-printed microscope attachment. The researchers then fed the pairs of corresponding images into a computer system that "learns" how to rapidly enhance the mobile phone images. The process relies on a deep-learning-based computer code, which was developed by the UCLA researchers.

To see if their technique would work on other types of lower-quality images, the researchers used deep learning to successfully perform similar transformations with images that had lost some detail because they were compressed for either faster transmission over a computer network or more efficient storage.


The study was published in ACS Photonics, a journal of the American Chemical Society. It builds upon previous studies by Ozcan's group that used deep learning to reconstruct holograms and improve microscopy.

Ozcan also holds a faculty appointment in the department of surgery at the David Geffen School of Medicine at UCLA, and is an associate director of the California NanoSystems Institute.

The other authors of the paper are Hatice Ceylan Koydemir, Hongda Wang, Zhensong Wei, Zhengshuang Ren, Harun Günayd?n, Yibo Zhang, Zoltán Göröcs, Kyle Liang and Derek Tseng, all of UCLA.

The research was supported by the National Science Foundation and the Howard Hughes Medical Institute.

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