Method, electronic device, and computer program product for training image processing model

An electronic device, comprising:

• at least one processor;
• and
• at least one memory, the at least one memory being coupled to the at least one processor and storing instructions for execution by the at least one processor, wherein the instructions, when executed by the at least one processor, cause the electronic device to perform actions comprising:
• embedding a pre-trained generator into an image processing model;
• and
• training the image processing model using a plurality of pairs of sample images, wherein at least one pair of sample images of the plurality of pairs of sample images comprises a first sample image having a first resolution and a second sample image having a second resolution, and wherein the first resolution is less than the second resolution;
• wherein training the image processing model using a plurality of pairs of sample images comprises:
• inputting the first sample image to an encoder in the image processing model;
• obtaining mapped sample image features by mapping sample image features output by the encoder;
• inputting the mapped sample image features to the pre-trained generator to obtain a first output image;
• and
• training the image processing model based on the first sample image, the first output image, and the second sample image;
• wherein training the image processing model based on the first sample image, the first output image, and the second sample image further comprises:
• determining an adversarial loss function for the image processing model using a discriminator corresponding to the pre-trained generator based on the first output image and the second sample image;
• determining at least one of (i) a first loss function for the image processing model based on the first sample image and the second sample image, and (ii) a second loss function for the image processing model using the pre-trained generator and the discriminator based on the first sample image and the second sample image;
• and
• constructing a weighted loss function using the adversarial loss function and the at least one of the first loss function and the second loss function to train the image processing model.

A computer program product comprising a non-transitory computer-readable medium having machine-executable instructions stored therein, wherein the machine-executable instructions, when executed by a machine, cause the machine to perform actions comprising:

• embedding a pre-trained generator into an image processing model;
• and
• training the image processing model using a plurality of pairs of sample images, wherein at least one pair of sample images of the plurality of pairs of sample images comprises a first sample image having a first resolution and a second sample image having a second resolution, and wherein the first resolution is less than the second resolution;
• wherein training the image processing model using a plurality of pairs of sample images comprises:
• inputting the first sample image to an encoder in the image processing model;
• obtaining mapped sample image features by mapping sample image features output by the encoder;
• inputting the mapped sample image features to the pre-trained generator to obtain a first output image;
• and
• training the image processing model based on the first sample image, the first output image, and the second sample image;
• wherein training the image processing model based on the first sample image, the first output image, and the second sample image further comprises:
• determining an adversarial loss function for the image processing model using a discriminator corresponding to the pre-trained generator based on the first output image and the second sample image;
• determining at least one of (i) a first loss function for the image processing model based on the first sample image and the second sample image, and (ii) a second loss function for the image processing model using the pre-trained generator and the discriminator based on the first sample image and the second sample image;
• and
• constructing a weighted loss function using the adversarial loss function and the at least one of the first loss function and the second loss function to train the image processing model.