Research on image style transfer method based on improved generative adversarial network
In order to solve the problem of the traditional Generative Adversarial Network image-style transfer limited to the pairing data set,and the problems of poor performance and slow training when CycleGAN learn advanced features,the paper uses the ModileNetv2-CycleGAN model for image style transfer,and introduces multiscale structural similarity index loss as a characteristic of punishment items retains style pictures to improve the effect of characteristic learning,thereby improving the quality of style pictures.Objective structure similar to SSIM and peak signal-to-noise ratio PSNR and subjective voting as evalu-ation indicators are adopted to evaluate the effect of transfer.The experimental results show that using ModileNetv2-CycleGAN and MS-SSIM Loss can improve style migration quality and have better visual effects.
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