Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …

With the development of generative adversarial networks (GANs), recent face restoration
(FR) methods often utilize pre-trained GAN models (ie,, StyleGAN2) as prior to generate rich …

Deep Neural Networks (DNNs) have achieved impressive results on the task of image
denoising, but there are two serious problems. First, the denoising ability of DNNs-based …

As a powerful statistical signal modeling technique, sparse representation has been widely
used in various image restoration (IR) applications. The sparsity-based methods have …

In the three-dimensional video system, the depth image-based rendering is a key technique
for generating synthesized views, which provides audiences with depth perception and …

High-quality magnetic resonance (MR) images afford more detailed information for reliable
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …

Convolutional neural networks (CNNs) have demonstrated excellent capability in various
visual recognition tasks but impose an excessive computational burden. The latter problem …

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

As face editing scenarios have become popular, the face inpainting technique has become
a hot topic. Although some existing methods can inpaint faces with preserved identity …

Deep learning-based methods have dominated the field of image denoising with their
superior performance. Most of them belong to the non-blind denoising approaches …