Image inpainting is a significant task in the applications of computer vision, that aims to fill in
damaged regions with visually realistic contents. With the development of deep learning …

This article presents a neural network-based solution to recover pixels occluded by clouds in
satellite images. We leverage radio frequency (RF) signals in the ultrahigh-/superhigh …

The majority of existing face inpainting methods primarily focus on generating a single result
that visually resembles the original image. The generation of diverse and plausible results …

In this article, we focus on the facial completion task based on the parsing feature map. In
recent years, methods based on deep learning have achieved remarkable results in face …

Realistic representation of building walls from images is an important aspect of scene
understanding and has many applications. Often, images of buildings are the only input for …

The paper outlines a pipeline for the removal of transient objects from orthophotos to
enhance the clarity and utility for orthophotos in military and civilian geo-databases …

Recent image inpainting approaches based on deep convolutional neural networks have
achieved promising results. However, existing methods fail to consider the global and local …

Recent image inpainting methods based on end-to-end models have achieved great
success with the help of generative adversarial training and structure generation. However …

Recent works in image inpainting have shown that structural information plays an important
role in recovering visually pleasing results. In this paper, we propose an end-to-end …

In recent years, deep learning has become the mainstream method of image inpainting. It
can not only repair the texture of the image, obtain high-level abstract features of the image …