Year: 2018
Type: article
Abstract: Mobile phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile phones are not designed for microscopy and produce distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised, and color-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth of field. After training a convolutional neural network, we successfully imaged various samples, including human tissue sections and Papanicolaou and blood smears, where the recorded images were highly compressed to ease storage and transmission. This method is applicable to other low-cost, aberrated imaging systems and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications.
Source: ACS Photonics
Authors: Yair Rivenson, Hatice Ceylan Koydemir, Hongda Wang, Zhensong Wei, Zhengshuang Ren, Harun Günaydın, Yibo Zhang, Zoltán Göröcs, Kyle Liang, Derek Tseng, Aydogan Ozcan
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Howard Hughes Medical Institute, Mary Kay Foundation, King Abdullah University of Science and Technology, National Institutes of Health, Office of Naval Research, Division of Chemical, Bioengineering, Environmental, and Transport Systems, Division of Emerging Frontiers in Research and Innovation, Army Research Office, H2020 Marie Skłodowska-Curie Actions
OpenAlex ID: W2774993480