Design and experimental research of on device style transfer models for mobile environments

This study develops a neural style transfer (NST) model optimized for real-time execution on mobile devices through on-device AI, eliminating reliance on cloud servers. By embedding AI models directly into mobile hardware, this approach reduces operational costs and enhances user privacy. However, designing deep learning models for mobile deployment presents a trade-off between computational efficiency and visual quality, as reducing model size often leads to performance degradation. To address this challenge, we propose a set of lightweight NST models incorporating depthwise separable convolutions, residual bottlenecks, and optimized upsampling techniques inspired by MobileNet and ResNet architectures. Five model variations are designed and evaluated based on parameters, floating-point operations, memory usage, and image transformation quality. Experimental results demonstrate that our optimized models achieve a balance between efficiency and performance, enabling high-quality real-time style transfer on resource-constrained mobile environments. These findings highlight the feasibility of deploying NST applications on mobile devices, paving the way for advancements in real-time artistic image processing in mobile photography, augmented reality, and creative applications.