Comparative Analysis of Image Inpainting Techniques

Image inpainting is a critical task in computer vision, aimed at restoring missing or damaged regions in images. Traditional methods like Patch Match relied heavily on texture synthesis but often struggled with maintaining structural integrity, especially in complex or irregular gaps. The emergence of deep learning has transformed the field, with Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Partial Convolutions (PCNs) demonstrating remarkable improvements. These advanced techniques utilize feature extraction, adversarial training, and attention mechanisms to achieve more realistic and coherent results. This research explores and compares these approaches, assessing their strengths, limitations, and performance across diverse datasets.