MAGAN‐RT: A Lightweight Adversarial Style Transfer Network for Real‐Time Cartoonization on Low‐Power Edge Devices

Recent advances in neural style transfer (NST) and generative adversarial networks (GANs) have enabled photorealistic and artistic image stylization. However, deploying such models on resource‐constrained edge devices remains challenging due to their high computational and memory demands. In this paper, we propose MAGAN‐RT, a lightweight adversarial style transfer framework optimized for real‐time cartoon‐style transformation on low‐power mobile and embedded platforms. MAGAN‐RT integrates depthwise separable convolutions, inverted bottleneck residual blocks, and a multi‐scale perceptual distillation strategy with auxiliary RGB supervision to enable efficient and expressive stylization. Furthermore, a real‐image‐based adversarial loss is employed to enhance realism while avoiding the artifacts commonly inherited from teacher models. Experimental results demonstrate that MAGAN‐RT outperforms existing lightweight and mobile‐compatible style transfer networks in both visual quality and runtime efficiency. It achieves state‐of‐the‐art LPIPS, FID, and SSIM scores, while maintaining sub‐10 ms inference latency on commercial smartphones, making it suitable for real‐time applications such as mobile AR and video filters.