Training method for a system for de-noising images

A training method for a system for de-noising images, the system comprising an input interface and a machine learning model having an architecture designed for de-noising images provided by the input interface, the training method comprising:

• providing numerous image datasets as input for the system, wherein each image dataset comprises a plurality of complex-valued image repetitions;
• performing a phase correction on the image repetitions, wherein for each provided image repetition of an image dataset a phase-corrected signal image is calculated by amending the phase of the complex-valued image repetition such that the phases of the image repetitions of the image dataset are consistent and such that the signal image comprises signal contribution of the image repetition;
• calculating a noise map for an image dataset based on a standard deviation between the signal images of this image dataset;
• and
• training the machine learning model based on the signal images, the noise map, and a loss function based on Stein's unbiased risk estimator.