1. State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001; 2. College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001; 3. School of Information Engineering, HuangShan University, Huangshan 245041
Abstract：To address the issues of content distortion, artifact appearance, and insufficient utilization of frequency-domain characteristics in style transfer, a reversible flow network for style transfer based on frequency-domain enhanced adaptive channel attention and feature pyramid fusion(FECANet) is proposed. Based on the pre-trained VGG19 architecture, a reversible flow network is designed to reduce feature loss and ensure the integrity of content structure by leveraging its unbiased feature transfer mechanism. A frequency-domain enhanced adaptive channel attention module is developed to analyze the frequency-domain distribution of style images, and accurate correlations between content and style features are established to improve the stylization effect. Additionally, a feature pyramid fusion scheme is designed to align global style with local textures, enhancing the coordination of transfer results. Experiments on MS-COCO and WikiArt datasets show that FECANet effectively balances style transfer and content preservation, and it shows superior performance in content structure integrity, stylization effect and computational efficiency.
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2026, Vol. 39 
