Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. The technique improves the resolution and color details of smartphone images so much that they approach the quality of images from laboratory-grade microscopes.

Image of a blood smear from a cell phone camera (left), following enhancement by the algorithm (center), and taken by a lab microscope (right).

The advance could help bring high-quality medical diagnostics into resource-poor regions, where people otherwise do not have access to high-end diagnostic technologies. And the technique uses attachments that can be inexpensively produced with a 3-D printer, at less than $100 a piece, versus the thousands of dollars it would cost to buy laboratory-grade equipment that produces images of similar quality.

Cameras on today's smartphones are designed to photograph people and scenery, not to produce high-resolution microscopic images. So, the researchers developed an attachment that can be placed over the smartphone lens to increase the resolution and the visibility of tiny details of the images they take, down to a scale of approximately one millionth of a meter. But that only solved part of the challenge because no attachment would be enough to compensate for the difference in quality between smartphone cameras’ image sensors and lenses and those of high-end lab equipment. The new technique compensates for the difference by using artificial intelligence to reproduce the level of resolution and color details needed for a laboratory analysis.

The researchers believe their approach is broadly applicable to other low-cost microscopy systems that use, for example, inexpensive lenses or cameras, and could facilitate the replacement of high-end bench-top microscopes with cost-effective, mobile alternatives. They shot images of lung tissue samples, blood, and Pap smears, first using a standard laboratory-grade microscope, and then with a smartphone equipped with the 3D-printed microscope attachment. Pairs of corresponding images were then fed into a computer system that “learns” how to rapidly enhance the mobile phone images. The process relies on a deep-learning–based computer code developed by the UCLA researchers.

To see if their technique would work on other types of lower-quality images, they 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 for more efficient storage.

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Photonics & Imaging Technology Magazine

This article first appeared in the September, 2018 issue of Photonics & Imaging Technology Magazine.

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