Deep Learning-based Painting Style Migration Algorithm and Its Visualization and Analysis
Jiayin Liu · WSEAS TRANSACTIONS ON COMPUTER RESEARCH · 2026
Computer graphics has made significant strides in the digital age, making image style transfer an exciting area of research. This study focuses on deep learning-based painting style transfer algorithms. By analysing classic style transfer algorithms such as GETIS and Johnson, we propose an improved algorithm based on attention mechanisms and multi-scale feature fusion. The results show that in terms of content-style balance, the improved algorithm based on the attention mechanism achieves a style fidelity score of 8 (compared to 7 for the Gatys algorithm), while the multi-scale fusion algorithm achieves a content retention score of 8. In terms of visual quality, the improved algorithm based on multi-scale fusion achieves an SSIM of 0.85, a PSNR of 33, and a MOS of 4.4, all of which outperform the classical algorithms.