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Field-Portable Pixel Super-Resolution Colour Microscope

Overview of attention for article published in PLOS ONE, September 2013
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Title
Field-Portable Pixel Super-Resolution Colour Microscope
Published in
PLOS ONE, September 2013
DOI 10.1371/journal.pone.0076475
Pubmed ID
Authors

Alon Greenbaum, Najva Akbari, Alborz Feizi, Wei Luo, Aydogan Ozcan

Abstract

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm(2). This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate 'rainbow' like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings.

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Geographical breakdown

Country Count As %
Japan 2 2%
Denmark 1 <1%
Indonesia 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 111 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 22%
Researcher 25 21%
Student > Master 13 11%
Student > Bachelor 10 9%
Student > Doctoral Student 7 6%
Other 13 11%
Unknown 23 20%
Readers by discipline Count As %
Engineering 57 49%
Agricultural and Biological Sciences 10 9%
Physics and Astronomy 9 8%
Computer Science 5 4%
Chemistry 4 3%
Other 8 7%
Unknown 24 21%