A training method for an image processing model for processing blurry images, the image processing model at least comprising a first network and a second network; the first network and the second network being codec networks with different scales; the sizes of the scales corresponding to the measurements of the sharpness of to-be-processed blurry images; and the method comprising: obtaining (S301) a sample pair for training, the sample pair comprising a clear image and a blurry image corresponding to the clear image; and the sharpness of the clear image being greater than a preset threshold, and the sharpness of the blurry image being less than the preset threshold; calling (S302) the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating (S303) network parameters of the first network and/or network parameters of the second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; wherein the network parameters of the first network and the network parameters of the second network meet a selective sharing condition, and the selective sharing condition indicates the network parameters between the first network and the second network are shared or independent; wherein the network parameters comprise a feature extraction parameter and a feature transformation parameter; the selective sharing condition, when indicating the network parameters between the first network and the second network are shared, indicates that the feature transformation parameter of the first network and the feature transformation parameter of the second network are the shared network parameters; and the selective sharing condition, when indicating the network parameters between the first network and the second network are independent, indicates that the feature extraction parameter of the first network and the feature extraction parameter of the second network are the independent network parameters. obtaining (S301) a sample pair for training, the sample pair comprising a clear image and a blurry image corresponding to the clear image; and the sharpness of the clear image being greater than a preset threshold, and the sharpness of the blurry image being less than the preset threshold; calling (S302) the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating (S303) network parameters of the first network and/or network parameters of the second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; wherein the network parameters of the first network and the network parameters of the second network meet a selective sharing condition, and the selective sharing condition indicates the network parameters between the first network and the second network are shared or independent; wherein the network parameters comprise a feature extraction parameter and a feature transformation parameter; the selective sharing condition, when indicating the network parameters between the first network and the second network are shared, indicates that the feature transformation parameter of the first network and the feature transformation parameter of the second network are the shared network parameters; and the selective sharing condition, when indicating the network parameters between the first network and the second network are independent, indicates that the feature extraction parameter of the first network and the feature extraction parameter of the second network are the independent network parameters. <EOS>