Image Restoration with GANs: SRGAN and WGAN Fundamentals

Digital images often suffer from low resolution, noise, or missing details, but modern deep learning offers powerful solutions. Generative Adversarial Networks (GANs) have revolutionized how we restore and enhance visual data. This text-based course guides you from the fundamental math of generative models to implementing advanced image restoration techniques. You will understand how to rebuild degraded images, increase resolution, and fill in missing pixels with realistic textures. What you'll learn: • Understand the foundational architecture of Generative Adversarial Networks (GANs) and how generators and discriminators interact. • Implement Super-Resolution GANs (SRGAN) to upscale low-resolution images while preserving realistic details. • Apply Wasserstein GANs (WGAN) to improve training stability and avoid common generative model pitfalls. • Explore image inpainting techniques to seamlessly reconstruct missing or damaged parts of an image. • Evaluate restoration quality using modern metrics like PSNR, SSIM, and Fréchet Inception Distance (FID). You will start with the core concepts of generative modeling before moving on to step-by-step code implementations of SRGAN and WGAN architectures. The material concludes with practical evaluation strategies to measure the success of your restoration models. This course is designed for aspiring deep learning practitioners and computer vision enthusiasts who want a clear, conceptual, and code-focused introduction to image restoration. Basic familiarity with Python and neural networks is helpful, but no prior experience with GANs is required. Start reading today to master the foundations of neural image restoration.