US 12,266,162 B2
Enhancing contrast sensitivity and resolution in a grating interferometer by machine learning
Seung Wook Lee, Busan (KR); Seho Lee, Busan (KR); and Ge Wang, Loudonville, NY (US)
Assigned to RENSSELAER POLYTECHNIC INSTITUTE, Troy, NY (US)
Appl. No. 17/766,365
Filed by RENSSELAER POLYTECHNIC INSTITUTE, Troy, NY (US)
PCT Filed Oct. 8, 2019, PCT No. PCT/US2019/055189
§ 371(c)(1), (2) Date Apr. 4, 2022,
PCT Pub. No. WO2020/033979, PCT Pub. Date Feb. 13, 2020.
Claims priority of provisional application 62/910,670, filed on Oct. 4, 2019.
Claims priority of application No. 10-2018-0092483 (KR), filed on Aug. 8, 2018.
Prior Publication US 2024/0046629 A1, Feb. 8, 2024
Int. Cl. G06V 10/82 (2022.01); G02B 6/293 (2006.01); G06V 10/75 (2022.01)
CPC G06V 10/82 (2022.01) [G02B 6/29353 (2013.01); G06V 10/751 (2022.01); G06V 2201/03 (2022.01)] 9 Claims
OG exemplary drawing
 
1. An apparatus for enhancing contrast sensitivity and resolution in a grating interferometer by machine learning, the apparatus comprising:
a grating interferometer image acquisition unit configured to acquire a first image with a target resolution and a second image with a target contrast sensitivity from a symmetrical grating interferometer by linearly moving a position of a sample in the interferometer;
a numerical phantom generation unit configured to generate a numerical phantom for performing machine learning;
a convolution layer generation unit configured to perform calculation processing of a convolutional neural network to extract features from input data;
an activation function application calculation unit configured to apply a ReLu (Rectified linear unit) activation function to an output value of the convolution calculation to perform smooth repetitive machine learning;
a CNN (convolutional neural network) repetitive machine learning unit configured to correct a convolution calculation factor while repeatedly performing forward propagation and backward propagation processes; and
an image matching output unit configured to match and provide as output features extracted by repetitive machine learning of the CNN repetitive machine learning unit.