Image restoration method and apparatus, electronic device, and storage medium

A non-transitory computer-readable storage medium, storing computer-readable instructions, wherein the computer-readable instructions, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• acquiring an original low-light image to be restored;
• acquiring a pre-trained image processing model, wherein the pre-trained image processing model comprises an optical feature extraction network and an image feature extraction network, the optical feature extraction network comprises a convolution parameter determined based on an illumination code matrix and a convolution layer, and the image feature extraction network comprises a plurality of fully connected layers;
• and
• inputting the original low-light image into the pre-trained image processing model, so that the optical feature extraction network in the pre-trained image processing model extracts an illumination feature from the original low-light image, and the image feature extraction network extracts a target image feature from the original low-light image, and generating a target bright image based on the illumination feature and the target image feature.

An electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other via the communication bus;

• wherein:
• the memory is used for storing computer-readable instructions;
• and
• the processor is used for executing operations comprising:
• acquiring an original low-light image to be restored;
• acquiring a pre-trained image processing model, wherein the pre-trained image processing model comprises an optical feature extraction network and an image feature extraction network, the optical feature extraction network comprises a convolution parameter determined based on an illumination code matrix and a convolution layer, and the image feature extraction network comprises a plurality of fully connected layers;
• and
• inputting the original low-light image into the pre-trained image processing model, so that the optical feature extraction network in the pre-trained image processing model extracts an illumination feature from the original low-light image, and the image feature extraction network extracts a target image feature from the original low-light image, and generating a target bright image based on the illumination feature and the target image feature.