At present, most of the current image style transfer methods are supervised learning, the training data need to appear in pairs. And when the background of the image needs to be processed, the existing methods are too cumbersome. In order to solve these problems, this paper proposed a new method of unsupervised image style transfer based on image mask. In the experiment, it adopted the architecture of the cycle-consistent generative adversarial network, and used the Inception-ResNet structure as the basic component to design a new generative model with the built-in image mask. At last, it automatically reconstructed the background of the image and the learned abstract features through unsupervised learning. Experiments show that the proposed method can effectively separate and reconstruct the background and the learned abstract features of the image, and solves the problem of regional interference in the feature learning process, and achieves considerable visual effects.

[2020-08-05] Printed Article /doc/2019.03.0093/v2