DOI: 10.1101/478982Nov 26, 2018Paper

Generative adversarial network (GAN) enabled on-chip contact microscopy

BioRxiv : the Preprint Server for Biology
Xiongchao ChenPeng Fei

Abstract

We demonstrate a deep learning based contact imaging on a CMOS chip to achieve ~1 μm spatial resolution over a large field of view of ~24 mm^2. By using regular LED illumination, we acquire single lower-resolution image of the objects placed approximate to the sensor with unit fringe magnification. For the raw contact-mode lens-free image, the pixel size of the sensor chip limits the spatial resolution. We apply a generative and adversarial networks (GAN), a type of deep learning algorithm, to circumvent this limitation and effectively recover much higher resolution image of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of view (24 mm^2) due to unit magnification. This GAN-contact imaging approach eliminates the need of either lens or multi-frame acquisition, being very handy and cost-effective. We demonstrate the success of this approach by imaging the proliferation dynamics of cells directly cultured on the chip.

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