Generative Vision Hybrid Model Enhancing Realistic Synthetic Media Creation Through Advanced Image Processing Techniques

In recent years, synthetic media generation has gained significant attention in areas such as entertainment, gaming, and visual content creation. The Generative Vision Hybrid Model leverages advanced generative architectures and image processing techniques to create highly realistic synthetic media. By integrating StyleGAN with a vision enhancement layer comprising Autoencoder-based Attention and Transformer modules, the model achieves exceptional quality in image synthesis. The dataset underwent robust preprocessing, including Wavelet Transform-based Noise Reduction and Adaptive Contrast Enhancement (ACE), ensuring clean and diverse inputs. The model achieved an Inception Score (IS) of 9.5, a Fréchet Inception Distance (FID) of 15.8, and a Structural Similarity Index (SSIM) of 0.94, outperforming existing benchmarks like StyleGAN2. Human evaluators rated the model's output with an average realism score of 4.7/5, highlighting its superior perceptual quality. Additionally, PSNR reached 32.6 dB, demonstrating high fidelity. The results validate the effectiveness of the hybrid model in producing photorealistic synthetic media with fine-grained detail and structural consistency. Future work can extend this approach to multi-modal media synthesis, real-time applications, and ethical watermarking systems to ensure responsible usage.