Image Inpainting with GANs: A PyTorch Guide to Restoring Images

Damaged photos, blocked objects, and missing pixels can ruin important visual data. Generative image inpainting solves this by predicting and reconstructing missing regions with highly realistic textures and shapes. This text-based course guides you through the foundational math, architecture, and implementation of generative adversarial networks (GANs) dedicated to image restoration. You will read clear explanations, study step-by-step code snippets in PyTorch, and learn how to train models that fill image gaps seamlessly. What you'll learn: - Understand the fundamental concepts of image inpainting and convolutional neural networks. - Build generative adversarial networks (GANs) tailored for image restoration tasks. - Implement contextual and adversarial loss functions in PyTorch to guide the model. - Apply modern attention mechanisms to improve the coherence of reconstructed textures. - Evaluate inpainting quality using current industry metrics like FID and LPIPS. - Practice debugging and training deep learning models with written exercises. The course begins with key terminology and foundational concepts of image masking before moving into hands-on PyTorch implementations of generator and discriminator architectures. You will then explore training loops, loss optimization, and modern refinement techniques. This course is designed for beginners in deep learning and computer vision who have basic Python knowledge and want to learn generative modeling without complex prerequisites. Start reading today to build your first image restoration model from scratch.