Super-resolution generative adversarial network (SRGAN) enabled on-chip contact microscopy. (19th July 2021)

• Record Type: Journal Article
• Authors: Zhang, Hao; Zhu, Tingting; Chen, Xiongchao; Zhu, Lanxin; Jin, Di; Fei, Peng
• Is Part Of: Journal of physics. Volume 54:Number 39(2021)
• Publication Date: 2021-07-19
• Subjects: biophotonics -- optical imaging -- contact microscopy -- cell imaging; Physics -- Periodicals; 530
• DOI: 10.1088/1361-6463/ac1138
• Languages: English
• ISSNs: 0022-3727

Abstract

Abstract: We demonstrate a deep learning based contact imaging on a CMOS chip to achieve ∼1 μ m spatial resolution over a large field of view of ∼24 mm 2 . By using regular LED illumination, we acquire the 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 super-resolution generative adversarial networks, a type of deep learning based single-image super-resolution (SR) 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 SR contact imaging approach eliminates the need of either lens or multi-frame acquisition, being very powerful and cost-effective. We demonstrate the success of this approach by imaging the proliferation dynamics of large-scale cells and the Instantaneous behaviors of freely moving Caenorhabditis elegans directly on the chip.