METHOD FOR AUGMENTING IMAGE ON THE BASIS OF GENERATIVE ADVERSARIAL CASCADED NETWORK

摘要

Disclosed in the present invention is a method for augmenting an image on the basis of a generative adversarial cascaded network. The method comprises: determining a region of interest from an original image I ori and cutting same to obtain a cut image I cut ; obtaining an augmented data set S cut by pre-processing the I cut ; training an Ⅰ-level generative adversarial network by using the data set S cut ; loading the trained I-level generator, inputting random noise to infer an image, and performing up-sampling processing on the generated image to form a new data set S I ; using the data set S I and the I cut as the training data sets of an II-level generative adversarial network, and training the II-level generative adversarial network; loading the trained II-level generator, and inputting the data set S I into the II-level generator to infer a required augmented image I des . The present invention solves the problems of small difference and low resolution of generated images in the I-level generative adversarial network when image augmentation is performed, thereby improving the generalization performance of the network while performing image augmentation.