公开(公告)号：US12266162B2

公开(公告)日：2025-04-01

申请号：US17766365

申请日：2019-10-08

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Seung Wook Lee ,  Seho Lee ,  Ge Wang

IPC: G06V10/82 ,  G02B6/293 ,  G06V10/75

Abstract: The present disclosure relates to an apparatus for enhancing contrast sensitivity and resolution in a grating interferometer by machine learning, which can improve both image contrast sensitivity and spatial resolution in a grating interferometer by machine learning, the apparatus including: an image acquisition unit; a numerical phantom generation unit, a convolution layer generation unit to extract features from input data; an activation function application calculation unit that can apply a rectified linear activation function to an output value of the convolution calculation to perform smooth repetitive machine learning; a CNN repetitive machine learning unit that corrects a convolution calculation factor while repeatedly performing forward propagation and backward propagation processes; and an image matching output unit that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit.

公开(公告)号：US20240046629A1

公开(公告)日：2024-02-08

申请号：US17766365

申请日：2019-10-08

Applicant: RENSSELAER POLYTECHNIC INSTITUTE

Inventor： Seung Wook Lee ,  Seho Lee ,  Ge Wang

IPC: G06V10/82 ,  G06V10/75 ,  G02B6/293

CPC classification number: G06V10/82 ,  G06V10/751 ,  G02B6/29353 ,  G06V2201/03

Abstract: The present disclosure relates to an apparatus for enhancing contrast sensitivity and resolution in a grating interferometer by machine learning, which can improve both image contrast sensitivity and spatial resolution in a grating interferometer by machine learning, the apparatus including: an image acquisition unit; a numerical phantom generation unit, a convolution layer generation unit to extract features from input data; an activation function application calculation unit that can apply a rectified linear activation function to an output value of the convolution calculation to perform smooth repetitive machine learning; a CNN repetitive machine learning unit that corrects a convolution calculation factor while repeatedly performing forward propagation and backward propagation processes; and an image matching output unit that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit.