Illustration image style transfer method design based on improved cyclic consistent adversarial network

Abstract

To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. Through a series of quantitative and qualitative experiments, high-quality style transfer is achieved, especially while retaining the original features of the illustration. The results show that when running on the Monet2photo dataset, when the system iterates to 72 times, the loss function value of the research method approaches the target value of 0.00. On the Horse2zebra dataset, as the sample size increases, the research method has the smallest FID value, and the value approaches 40.00 infinitely. With the change of peak signal-to-noise ratio, the accuracy of the research algorithm has been greater than 95.00%. Practical application found that the color of the image obtained by the research method is more gorgeous and the line features are more obvious. The above results all show that the research method has achieved more satisfactory results in the task of style transfer of illustration images, especially in terms of the accuracy of style transfer and the retention of image details.