An image processing method (300, 700) for generating a prediction result for an input image, comprising: extracting an input feature (311; 511; 611) from the input image (301; 501; 601; 701) using a feature extraction neural network model (310, 510, 610); generating one or more augmented features (622, 623, 624) using a feature augmentation model (320, 520, 620) comprising an encoding neural network model and a decoding neural network model, based on the input feature and a respective transformation parameter of one or more transformation parameters (321, 523, 621) indicating a type and a degree of a transformation that could be applied to an image, wherein the one or more transformation parameters correspond to different transformations, the generating comprising: generating of a first augmented feature of the one or more augmented features, comprising: encoding the input feature to a latent feature using the encoding neural network model (321; 521); combining the latent feature and a transformation code corresponding to the respective transformation parameter to determine a combined feature wherein combining the latent feature and the transformation code comprises concatenating the latent feature and the transformation code; and decoding the combined feature to the first augmented feature using the decoding neural network model; wherein the transformation code is the transformation parameter, converted into a form that may be processed in the decoding neural network model and includes a first field indicating the type of transformation and a second field indicating the degree of transformation; generating the prediction result (502, 602) based on the one or more augmented features using a prediction neural network model (330, 530, 630). extracting an input feature (311; 511; 611) from the input image (301; 501; 601; 701) using a feature extraction neural network model (310, 510, 610); generating one or more augmented features (622, 623, 624) using a feature augmentation model (320, 520, 620) comprising an encoding neural network model and a decoding neural network model, based on the input feature and a respective transformation parameter of one or more transformation parameters (321, 523, 621) indicating a type and a degree of a transformation that could be applied to an image, wherein the one or more transformation parameters correspond to different transformations, the generating comprising: generating of a first augmented feature of the one or more augmented features, comprising: encoding the input feature to a latent feature using the encoding neural network model (321; 521); combining the latent feature and a transformation code corresponding to the respective transformation parameter to determine a combined feature wherein combining the latent feature and the transformation code comprises concatenating the latent feature and the transformation code; and decoding the combined feature to the first augmented feature using the decoding neural network model; wherein the transformation code is the transformation parameter, converted into a form that may be processed in the decoding neural network model and includes a first field indicating the type of transformation and a second field indicating the degree of transformation; generating of a first augmented feature of the one or more augmented features, comprising: encoding the input feature to a latent feature using the encoding neural network model (321; 521); combining the latent feature and a transformation code corresponding to the respective transformation parameter to determine a combined feature wherein combining the latent feature and the transformation code comprises concatenating the latent feature and the transformation code; and decoding the combined feature to the first augmented feature using the decoding neural network model; wherein the transformation code is the transformation parameter, converted into a form that may be processed in the decoding neural network model and includes a first field indicating the type of transformation and a second field indicating the degree of transformation; encoding the input feature to a latent feature using the encoding neural network model (321; 521); combining the latent feature and a transformation code corresponding to the respective transformation parameter to determine a combined feature wherein combining the latent feature and the transformation code comprises concatenating the latent feature and the transformation code; and decoding the combined feature to the first augmented feature using the decoding neural network model; wherein the transformation code is the transformation parameter, converted into a form that may be processed in the decoding neural network model and includes a first field indicating the type of transformation and a second field indicating the degree of transformation; generating the prediction result (502, 602) based on the one or more augmented features using a prediction neural network model (330, 530, 630).