Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper interaction with image …

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

Image inpainting has made significant advances in recent years. However, it is still
challenging to recover corrupted images with both vivid textures and reasonable structures …

Numerous task-specific variants of conditional generative adversarial networks have been
developed for image completion. Yet, a serious limitation remains that all existing algorithms …

To defend against manipulation of image content, such as splicing, copy-move, and
removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to …

Image Inpainting is an age-old image processing problem, with people from different eras
attempting to solve it using various methods. Traditional image inpainting algorithms have …

Deep image inpainting has made impressive progress with recent advances in image
generation and processing algorithms. We claim that the performance of inpainting …

Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but
still fails to handle large missing area or complex semantics. Recently, GAN inversion based …

Many recent inpainting works have achieved impressive results by leveraging Deep Neural
Networks (DNNs) to model various prior information for image restoration. Unfortunately, the …

Robot harvesting has emerged as an urgent need for apple industry due to a sharp decline
in agricultural labor. In the field of harvesting robots, the use of multiple robotic arms to …