US 12,008,737 B2
Deep learning model for noise reduction in low SNR imaging conditions
Denis Sharoukhov, Brooklyn, NY (US); Tonislav Ivanov, Brooklyn, NY (US); and Jonathan Lee, New York, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Aug. 5, 2021, as Appl. No. 17/444,499.
Claims priority of provisional application 63/062,589, filed on Aug. 7, 2020.
Prior Publication US 2022/0044362 A1, Feb. 10, 2022
Int. Cl. G06T 5/70 (2024.01); G06T 3/4046 (2024.01); G06T 5/50 (2006.01)
CPC G06T 5/70 (2024.01) [G06T 3/4046 (2013.01); G06T 5/50 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
an imaging apparatus configured to perform darkfield imaging of a specimen; and
a computing system in communication with the imaging apparatus, the computing system comprising one or more processors and a memory, the memory having programming coded thereon, which, when executed by the one or more processors, causes the computing system to perform operations comprising:
obtaining, by the computing system, a plurality of noisy images of the specimen captured by the imaging apparatus using darkfield imaging, wherein the plurality of noisy images includes at least two images of the specimen captured using darkfield imaging;
denoising, by the computing system, the plurality of noisy images by inputting the plurality of noisy images of the specimen into a convolutional neural network trained to output a single denoised image of the specimen; and
generating, as output from the convolutional neural network, the single denoised image of the specimen.