Deep learning (DL) has demonstrated its powerful capabilities in the field of image
inpainting. The DL-based image inpainting approaches can produce visually plausible …

Deep learning-based image inpainting has been widely studied, leading to great success.
However, many methods adopt convolution and normalization operations, which will bring …

Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …

Deep learning based image inpainting methods have recently made significant
improvements, which can now generate visually realistic and semantically plausible pixels …

The latest deep learning-based approaches have shown promising results for the
challenging task of inpainting missing regions of an image. However, the existing methods …

Deep learning-based methods have recently shown promising results in image inpainting.
These methods generate patches with visually plausible image structures and textures …

As deep learning technology develops rapidly, image inpainting methods have made
significant progress in generating reasonable contents for images with large and irregular …

Inpainting methods aim to restore missing parts of corrupted images and play a critical role
in many computer vision applications, such as object removal and image restoration …

Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for
producing plausible results. However, in most existing methods, eg, context encoder, the …

Image inpainting, which aims to fill the missing holes of the images, is a challenging task
because the holes may contain complicated structures or different possible layouts. Deep …