The deep learning-based image inpainting models discussed in this review are critical
image processing techniques for filling in missing or removed regions in static planar …

Blind image inpainting is a crucial restoration task that does not demand additional mask
information to restore the corrupted regions. Yet, it is a very less explored research area due …

Online media data, in the forms of images and videos, are becoming mainstream
communication channels. However, recent advances in deep learning, particularly deep …

Proper inference of semantics is necessary for realistic image inpainting. Most image
inpainting methods use deep generative models, which require large image datasets to …

Face inpainting, the technique of restoring missing or damaged regions in facial images, is
pivotal for applications like face recognition in occluded scenarios and image analysis with …

Face restoration (FR) is a specialized field within image restoration that aims to recover low-
quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep …

Although image inpainting is a challenging task in computer vision, most existing image
inpainting methods have achieved remarkable progress. However, occlusion and low …

Neural networks for visual content understanding have recently evolved from convolutional
ones to transformers. The prior (CNN) relies on small-windowed kernels to capture the …

Deep learning-based inpainting models have achieved success in restoring natural images,
yet their application to ancient paintings encounters challenges due to the loss of texture …

Image inpainting consists of filling holes or missing parts of an image. Inpainting face
images with symmetric characteristics is more challenging than inpainting a natural scene …