Article: Artistic style image migration model based on cycle-consistent generative adversarial networks Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.16 pp.53 - 68 Abstract: Art style image migration has been increasingly important in image processing research as computer vision and deep learning technologies develop. Most well-known style migration techniques depend on paired training data, which is occasionally difficult to get in practice. Many solutions for complex art forms lose material or have style inconsistencies, which makes it challenging to fulfil high-quality content preservation. Regarding the mentioned problems, this work presents ArtCycleGAN, a cycle-consistent generative adversarial network-based art style image migration model. Pre-trained VGG19 network perceptual loss and cycle-consistent loss help to enable high quality unsupervised art style migration. ArtCycleGAN proves its validity in art style migration by experimental findings showing good performance in style similarity, content retention, and perceptual quality. This work presents a dependable and efficient approach for unsupervised art style migration as well as fresh ideas and references for picture synthesis applied with generative adversarial networks. Inderscience Publishers - linking academia, business and industry through research

Title: Artistic style image migration model based on cycle-consistent generative adversarial networks

Authors: Laixi Zheng

Addresses: School of Painting and Drawing, Guangzhou Academy of Fine Arts, Guangdong 510261, China

Abstract: Art style image migration has been increasingly important in image processing research as computer vision and deep learning technologies develop. Most well-known style migration techniques depend on paired training data, which is occasionally difficult to get in practice. Many solutions for complex art forms lose material or have style inconsistencies, which makes it challenging to fulfil high-quality content preservation. Regarding the mentioned problems, this work presents ArtCycleGAN, a cycle-consistent generative adversarial network-based art style image migration model. Pre-trained VGG19 network perceptual loss and cycle-consistent loss help to enable high quality unsupervised art style migration. ArtCycleGAN proves its validity in art style migration by experimental findings showing good performance in style similarity, content retention, and perceptual quality. This work presents a dependable and efficient approach for unsupervised art style migration as well as fresh ideas and references for picture synthesis applied with generative adversarial networks.

Keywords: art style image migration; CycleGAN; perceptual loss; unsupervised learning; image generation; content retention.

DOI: 10.1504/IJICT.2025.146377

International Journal of Information and Communication Technology, 2025 Vol.26 No.16, pp.53 - 68

Received: 27 Mar 2025
Accepted: 10 Apr 2025
Published online: 27 May 2025 *

Title: Artistic style image migration model based on cycle-consistent generative adversarial networks

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