Applying deep learning for style transfer in digital art: enhancing creative expression through neural networks

Neural style transfer (NST) has opened new possibilities for digital art by enabling the blending of distinct artistic styles with content from various images. However, traditional NST methods often need help balancing style fidelity and content preservation, and many models need more computational efficiency, limiting their applicability for real-time applications. This study aims to enhance the efficiency and quality of NST by proposing a refined model that addresses key challenges in content retention, style fidelity, and computational performance. Specifically, the research explores techniques to improve the visual coherence of style transfer, ensuring consistency and accessibility for practical use. The proposed model integrates Adaptive Instance Normalization (AdaIN) and Gram matrix-based style representation within a convolutional neural network (CNN) architecture. The model is evaluated using quantitative metrics such as content loss, style loss, Structural Similarity Index (SSIM), and processing time, along with a qualitative assessment of content and style consistency across various image pairs. The proposed model significantly improves content and style balance, with content and style loss values reduced by 15% compared to baseline models. The optimal configuration yields an SSIM score of 0.88 for medium style intensity, maintaining structural integrity while achieving stylistic effects. Additionally, the model's processing time is reduced by 76%, making it suitable for near-real-time applications. Style fidelity scores remain high across various artistic styles, with minimal loss in content retention. The refined NST model balances style and content effectively, enhancing visual quality and computational efficiency. These advancements make NST more accessible for real-time artistic applications, providing a versatile digital art, design, and multimedia production tool.