Design and implementation of style transfer system based on CycleGAN
In response to the complexity and inefficiency caused by the use of VGG16/VGG19 or traditional texture imitation in existing style transfer systems,the aim of this paper is to create a user-friendly,high-performance,and highly interactive deep learning-based style transfer prototype system.The system was designed to popularize its use among the general public and stimulate creative production in the professional art field.Taking the impressionistic style as an example,using CycleGAN,SSIM index and user subjective evaluation for effect measurement,all of them have different magnitudes of improvement compared with the traditional VGG19,with the lowest improvement of 29.33%and the highest improvement of 153.52%in the test group.In addition,for the user side,a style migration prototype system was developed,which allows users to realize the impressionistic style migration of hand-drawn works and uploaded images through an easy-to-use interface.The system display and user experience were optimized and received positive feedback from users.These results show its great potential in the field of enhancing users'aesthetic awareness and computer-aided design,and provide new ideas for future aesthetic education and artistic creation.
style transferCycleGANdeep learningsystem designprototype system
NETL
NSTL
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