X-ray imaging restoration using deep learning algorithms

A method for image restoration, comprising:

• acquiring, in a first process, a plurality of first X-ray images along a time sequence by detecting X-rays irradiated from an X-ray tube and passed through a subject using an X-ray detector;
• inputting one of the plurality of first X-ray images to a learned model each time when the one of the plurality of first X-ray images is acquired;
• and
• displaying, in a second process, an X-ray image, output from the learned model, with less noise and blur each time when the X-ray image is output, wherein the learned model is trained by using a second X-ray image as input learning data and a third X-ray image with less noise and blur than the second X-ray image as output learning data,
• wherein the second process of displaying the output X-ray image is performed in parallel with the first process of acquiring the first X-ray images.

A non-transitory computer-readable medium including computer-readable instructions that, when executed by a computing system, cause the computing system to sort data by performing a method comprising:

• acquiring, in a first process, a plurality of first X-ray images along a time sequence by detecting X-rays irradiated from an X-ray tube and passed through a subject using an X-ray detector;
• inputting one of the plurality of first X-ray images to a learned model each time when the one of the plurality of first X-ray images is acquired;
• and
• displaying, in a second process, an X-ray image output from the learned model with less noise and blur each time when the image is output, wherein the learned model is trained by using a second X-ray image as input learning data and a third X-ray image with less noise and blur than the second X-ray image as output learning data,
• wherein the second process of displaying the output X-ray image is performed in parallel with the first process of acquiring the first X-ray images.