The invention discloses an image processing method for expanding a data set under a small sample. In order to solve the problem of poor identification accuracy of a small sample image, the invention adopts a sample expansion technology to overcome the problem caused by insufficient samples, and provides a more general image processing method. The method comprises the steps that the original training samples are translated, rotated, mirrored, scaled and transmitted, the contrast and the brightness are transformed, the noise is added, the virtual training samples are generated, the number of training samples is increased by generating virtual samples, and then the virtual samples are fused with the original training samples. Through a large number of experiments, the method of the inventionhas excellent recognition effect on a small sample training set, and has better recognition performance. If the sample is insufficient, then the image features in the training phase are not enough toeffectively represent the image features changes, so that the difficulty of image recognition is increased, and even the phenomenon that the image can not be recognized appears.