Image Completion

Image completion, also known as image inpainting, aims to reconstruct missing or damaged regions in images by generating plausible and semantically coherent content. Current research heavily utilizes deep learning models, particularly generative adversarial networks (GANs) and transformers, often incorporating techniques like diffusion models and vector quantization to improve both the speed and quality of image generation, including handling diverse and large-scale missing regions. This field is significant for its applications in image restoration, editing, and medical imaging, where it can help recover lost data and improve the quality of diagnostic images. Furthermore, advancements in image completion contribute to a broader understanding of image generation and representation learning.