An image processing method, performed by a computer device, wherein the method comprises: training an initial network, to obtain a first network, the first network being used for obtaining, based on a first sample image, a plurality of first processing images having definitions greater than that of the first sample image; training the first network, to obtain a second network, the second network being used for obtaining, based on at least one second sample image, a plurality of second processing images having definitions greater than that of the at least one second sample image and including a motion difference; training the second network, to obtain a third network, the third network being used for obtaining, based on at least one third sample image, a plurality of third processing images having definitions greater than that of the at least one third sample image, including a motion difference, and recovering hidden information in the at least one third sample image; and processing an original image according to the third network in response to receiving an image processing instruction, to obtain a plurality of target images; characterized in that, the training the first network, to obtain the second network comprises: inputting the at least one second sample image into the first network, and outputting the plurality of second processing images for each of the at least one second sample image; modifying a parameter of the first network by using a back-propagation algorithm when function values of the plurality of second processing images in a second loss function are greater than a second target value, the second loss function being used for ensuring that the definitions of the plurality of second processing images are greater than that of the at least one second sample image and including the motion difference; and obtaining the second network in accordance with a determination that a function value of the second loss function is less than or equal to the second target value. training an initial network, to obtain a first network, the first network being used for obtaining, based on a first sample image, a plurality of first processing images having definitions greater than that of the first sample image; training the first network, to obtain a second network, the second network being used for obtaining, based on at least one second sample image, a plurality of second processing images having definitions greater than that of the at least one second sample image and including a motion difference; training the second network, to obtain a third network, the third network being used for obtaining, based on at least one third sample image, a plurality of third processing images having definitions greater than that of the at least one third sample image, including a motion difference, and recovering hidden information in the at least one third sample image; and processing an original image according to the third network in response to receiving an image processing instruction, to obtain a plurality of target images; characterized in that, the training the first network, to obtain the second network comprises: inputting the at least one second sample image into the first network, and outputting the plurality of second processing images for each of the at least one second sample image; modifying a parameter of the first network by using a back-propagation algorithm when function values of the plurality of second processing images in a second loss function are greater than a second target value, the second loss function being used for ensuring that the definitions of the plurality of second processing images are greater than that of the at least one second sample image and including the motion difference; and obtaining the second network in accordance with a determination that a function value of the second loss function is less than or equal to the second target value. inputting the at least one second sample image into the first network, and outputting the plurality of second processing images for each of the at least one second sample image; modifying a parameter of the first network by using a back-propagation algorithm when function values of the plurality of second processing images in a second loss function are greater than a second target value, the second loss function being used for ensuring that the definitions of the plurality of second processing images are greater than that of the at least one second sample image and including the motion difference; and obtaining the second network in accordance with a determination that a function value of the second loss function is less than or equal to the second target value.