Method and electronic device for training image processing model and method and electronic device for processing images using image processing model

A training method of an image processing model, the training method comprising:

• obtaining a first training sample, wherein the first training sample comprises a training image and a first training label corresponding to the training image, the training image comprises a low-resolution image and the first training label corresponds to a first high-resolution image of a corresponding training image;
• obtaining a first model by training an initial model based on the first training sample;
• obtaining information corresponding to transfer learning of the first model;
• obtaining a second training sample comprising a training image group and a second training label corresponding to the training image group, the training image group comprising a plurality of low-resolution images corresponding to a plurality of different viewpoints of a same scene, and the second training label corresponding to a second high-resolution image of a corresponding training image group;
• and
• obtaining an image processing model by training a second model based on the second training sample, wherein the second model is configured based on the information corresponding to transfer learning of the first model.

An electronic device comprising:

• at least one processor;
• and
• at least one memory storing a computer program,
• wherein the at least one processor is configured to execute the computer program to:
• obtain an input image group, the input image group comprising a plurality of low-resolution images corresponding to a plurality of different viewpoints;
• obtain a feature of low-resolution images by extracting a feature for each low-resolution image of the plurality of low-resolution images of the input image group;
• obtain a fusion residual feature by fusing the feature of low-resolution images;
• and
• obtain a super-resolution image corresponding to the input image group based on the fusion residual feature.