Research on Style Transfer of Artistic Images Based on Artificial Intelligence

Abstract

In order to solve some inherent defects of generative confrontation network model in artistic image processing, such as unstable training, gradient disappearance and pattern collapse, the research on artistic image style transfer based on artificial intelligence generative confrontation network is put forward (overall method). On the basis of generative countermeasure network, the network model structure is optimized, and the spectral normalization processing is introduced and the residual structure is improved. Normalization constrains the parameter learning range of the network model, accelerates the learning speed of the model, and has a certain regularization effect; In the generator network, a new residual structure is used to optimize the signal propagation and reduce the training error. Through the treatment of identity mapping, ResNet network alleviates the problems of gradient disappearance and network degradation caused by deep network layers. The experimental results show that 10 subjects, aged between 22 and 30, including 3 females and 7 males, were invited to participate in the test. They had never been exposed to 30 pictures in the two groups, and the corresponding model of each group was unknown. From the results, the stylized pictures using the model proposed in this paper are more popular among young people, with an evaluation rate of 58% and better visual experience. Conclusion: Experiments show that the improved algorithm is better than the original algorithm in image style conversion.