Radiographic image processing method, machine-learning method, trained model, machine-learning preprocessing method, radiographic image processing module, radiographic image processing program, and radiographic image processing system

A radiographic image processing module comprising a processor configured to:

• acquire a radiographic image obtained by irradiating a subject with radiation and capturing an image of the radiation passing through the subject;
• convert a pixel value of each pixel in the radiographic image into a standard deviation of a noise value on the basis of relationship data indicating a relationship between the pixel value and the standard deviation, and generate a noise map that is data in which the converted standard deviation is associated with each pixel in the radiographic image;
• and
• input the radiographic image and the noise standard deviation map in parallel to a trained model constructed in advance through machine learning and execute image processing of removing noise from the radiographic image.

A radiographic image processing program causing a processor to function as:

• acquiring a radiographic image obtained by irradiating a subject with radiation and capturing an image of the radiation passing through the subject;
• converting a pixel value of each pixel in the radiographic image into a standard deviation of a noise value on the basis of relationship data indicating a relationship between the pixel value and the standard deviation, and generating a noise map that is data in which the converted standard deviation is associated with each pixel in the radiographic image;
• and
• inputting the radiographic image and the noise standard deviation map in parallel to a trained model constructed in advance through machine learning and executing image processing of removing noise from the radiographic image.