Abstract: Image inpainting, a critical task in computer vision, involves the art of replenishing missing or damaged regions within images. It’s a process that hinges on the capabilities of deep learning, primarily through the utilization of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The fundamental steps in implementing image inpainting encompass data collection and pre-processing, which entails assembling a dataset of images featuring gaps alongside their intact counterparts. The neural network architecture plays a pivotal role, with choices ranging from GANs to Auto-encoders, tailored to the specific task at hand. The models are trained by minimizing various loss functions, each contributing to specific training objectives. Inpainting algorithms must handle variable hole sizes and exhibit contextual understanding, ensuring generated content seamlessly blends with the surrounding context. Post-processing techniques can refine the generated inpaintings and evaluations are performed using quantitative metrics and qualitative assessments. Overall, deep learning-based image inpainting continues to advance, with practical applications in image restoration, object removal, and beyond.
Keywords: I Image inpainting, computer vision, Deep learning, Convolutional Neural Network, Generative Adversarial Network, dataset, Auto-encoders, image restoration, object removal.
Copyright Form