Patent ID: 11727670

Abstract:
A computer implemented method including acquiring a live image of a subject physical sample of a product or material; inputting the live image to a trained generator neural network to generate a defect-free reconstruction of the live image; comparing the defect-free reconstruction of the live image with the live image to determine a difference; and identifying a defect corresponding to the subject physical sample at a location of the determined difference. An unsupervised training of the generator neural network includes acquiring a set of images of the subject defect-free physical sample; executing a training phase including a plurality of training epochs, in which: training data images are synthesized by superimposing, onto each member of the set of images of subject defect-free physical sample as a respective parent image, defect image data; the synthesized training data images are reconstructed by the generator neural network which is iteratively trained to minimize a loss function between each reconstruction of the reconstructing of the synthesized training data images and the respective parent image of defect-free physical sample and increase an amount of difference between a training data image and the respective parent image caused by the superimposed defect image data from a minimum to a maximum as a function of a training condition.