Style Transfer of Chinese Art Works Based on Dual Channel Deep Learning Model

Aiming at the problems of style loss and lack of content in the style transfer of Chinese art works, this paper puts forward the style transfer technology of Chinese art works based on the dual channel deep learning model. On the basis of clarifying the technical principle of style transfer of art works, the image of art works is controlled and transformed based on the u-net network. The incomplete information in the restored image is filled, and the multiscale classification feature is used to calculate the color feature data items in the image. The sensitivity coefficient of color difference is calculated by using constraints, and the overlapping color discrimination and image segmentation of art images are realized. Poisson image editing is used to constrain the image spatial gradient to realize the style migration of art works. The experimental results show that this method can effectively avoid the problems of content error, distortion, and distortion in the process of art style migration, and has a better style migration effect.