Innovative Image Enhancement via GANs: Addressing Noise, Resolution, and Artifact Challenges

Generative adversarial networks (GANs) are increasingly used for image enhancement tasks like denoising, super-resolution, and artifact removal. In this study, we propose a robust GAN-based architecture featuring a U-Net-style generator and DCNN-based discriminator, optimized for real-time enhancement. Key contributions include multi-task image refinement using residual and attention modules. Our method is tested on paired datasets comprising low-light medical and artificially degraded images. Results show significant improvements in visual clarity, reduced noise, and improved resolution compared to traditional methods. Evaluation metrics such as inception score (IS) and Fréchet inception distance (FID) confirm the system’s performance. The optimal learning rate (0.0008) was empirically selected for training stability. These results validate the proposed GAN’s efficiency for practical deployment in domains requiring high-quality imaging.