An image processing method, implemented by a computer, comprising inputting an original image and a style reference image into a convolutional neural network to generate a first feature map of the original image and a second feature map of the style reference image; calculating respective correction values for respective pixel points in the first feature map based on the second feature map; replacing original values of the respective pixel points in the first feature map with the respective corrected values to obtain a third feature map; and performing a deconvolution process on the third feature map to obtain a processed image characterized by calculating the respective correction values for the respective pixel points in the first feature map based on the second feature map comprises: obtaining an attribute parameter of a feature block, the attribute parameter comprising: a shape parameter, a size parameter, and a base point parameter, the base point parameter referring to the position of the base point in the feature block; taking the respective pixel points in the second feature map as a base point, and dividing the second feature map into M second feature blocks according to the attribute parameter, wherein M is an amount of pixel points in the second feature map; taking the respective pixel points in the first feature map as a target base point, and obtaining a first feature block of the first feature map according to the attribute parameter; calculating M first Euclidean distances between the first feature block and each of the M second feature blocks, respectively; determining a second Euclidean distance which is shortest among the M first Euclidean distances; obtaining a first value of a respective pixel point corresponding to the target base point; obtaining a second value of a respective pixel point corresponding to a base point of the second feature block corresponding to the second Euclidean distance; calculating a weight value according to the first value and the second value; and determining the weight value as the correction value for the respective pixel points corresponding to the target base point. inputting an original image and a style reference image into a convolutional neural network to generate a first feature map of the original image and a second feature map of the style reference image; calculating respective correction values for respective pixel points in the first feature map based on the second feature map; replacing original values of the respective pixel points in the first feature map with the respective corrected values to obtain a third feature map; and performing a deconvolution process on the third feature map to obtain a processed image characterized by calculating the respective correction values for the respective pixel points in the first feature map based on the second feature map comprises: obtaining an attribute parameter of a feature block, the attribute parameter comprising: a shape parameter, a size parameter, and a base point parameter, the base point parameter referring to the position of the base point in the feature block; taking the respective pixel points in the second feature map as a base point, and dividing the second feature map into M second feature blocks according to the attribute parameter, wherein M is an amount of pixel points in the second feature map; taking the respective pixel points in the first feature map as a target base point, and obtaining a first feature block of the first feature map according to the attribute parameter; calculating M first Euclidean distances between the first feature block and each of the M second feature blocks, respectively; determining a second Euclidean distance which is shortest among the M first Euclidean distances; obtaining a first value of a respective pixel point corresponding to the target base point; obtaining a second value of a respective pixel point corresponding to a base point of the second feature block corresponding to the second Euclidean distance; calculating a weight value according to the first value and the second value; and determining the weight value as the correction value for the respective pixel points corresponding to the target base point.