Photorealistic Style Transfer Guided by Global Information

Different from artistic style transfer, the challenge of photorealistic style transfer is to maintain the authenticity of the output while transferring the color style of the style input.Now, most photorealistic style transfer methods perform pre-proces-sing or post-processing based on artistic style transfer methods, to maintain the authenticity of the output image.However, artistic style transfer methods usually cannot make full use of global color information to achieve a more coordinated overall impression, and pre-processing and post-processing operations are often tedious and time-consuming.To solve the above problems, this paper establishes a photorealistic style transfer network guided by global information, and proposes a color-partition-mean loss(Lcpm) to measure the similarity of the global color distribution between output and the style input.Adaptive instance normalization（AdaIN） is improved, and partition adaptive instance normalization（AdaIN-P） is proposed to better adapt to the color style tra