US 11,748,846 B2
Systems, devices, and methods for providing feedback on and improving the accuracy of super-resolution imaging
Matthew C. Putman, Brooklyn, NY (US); John B. Putman, Celebration, FL (US); Vadim Pinskiy, Wayne, NJ (US); and Joseph Succar, Brooklyn, NY (US)
Assigned to Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed by Nanotronics Imaging, Inc., Cuyahoga Falls, OH (US)
Filed on Apr. 5, 2021, as Appl. No. 17/222,425.
Application 17/222,425 is a continuation of application No. 17/029,703, filed on Sep. 23, 2020, granted, now 10,970,831.
Application 17/029,703 is a continuation of application No. 16/576,732, filed on Sep. 19, 2019, granted, now 10,789,695, issued on Sep. 29, 2020.
Application 16/576,732 is a continuation of application No. 16/233,258, filed on Dec. 27, 2018, granted, now 10,467,740, issued on Nov. 5, 2019.
Application 16/233,258 is a continuation of application No. 16/027,056, filed on Jul. 3, 2018, granted, now 10,169,852, issued on Jan. 1, 2019.
Prior Publication US 2021/0224966 A1, Jul. 22, 2021
Int. Cl. G03H 1/00 (2006.01); G06T 3/40 (2006.01); G06T 5/50 (2006.01); G06V 10/98 (2022.01); G06V 20/69 (2022.01); G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06V 10/764 (2022.01)
CPC G06T 3/4038 (2013.01) [G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06T 3/4053 (2013.01); G06T 5/50 (2013.01); G06V 10/764 (2022.01); G06V 10/993 (2022.01); G06V 20/693 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, by a computing system, a training data set comprising a plurality of high confidence super-resolution images and a plurality of low confidence super-resolution images;
generating, by the computing system, a simulated image classifier for determining a confidence interval that measures a likelihood of a super-resolution image falls into a particular class by:
inputting the training data set into a machine learning algorithm; and
learning, via the machine learning algorithm, to classify super-resolution images of artifacts;
obtaining, by the computing system, a target super-resolution image of a specimen;
analyzing, by the computing system via the simulated image classifier, the target super-resolution image to determine a class associated with the target super-resolution image; and
determining, by the computing system via the simulated image classifier, the class of the target super-resolution image and a confidence interval of the class.