Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Blind image deblurring is still a challenging problem due to the inherent ill-posed properties.
To improve the deblurring performance, many supervised methods have been proposed …

When a facial image is blurred, it significantly affects high-level vision tasks such as face
recognition. The purpose of facial image deblurring is to recover a clear image from a blurry …

This paper investigates the dependence of existing state-of-the-art person re-identification
models on the presence and visibility of human faces. We apply a face detection and …

In this paper, we propose an effective and efficient face deblurring algorithm by exploiting
semantic cues via deep convolutional neural networks. As the human faces are highly …

Recently, facial landmark detection algorithms have achieved remarkable performance on
static images. However, these algorithms are neither accurate nor stable in motion-blurred …

Conditional image generation lies at the heart of computer vision and conditional generative
adversarial networks (cGAN) have recently become the method of choice for this task, owing …

Most recent face deblurring methods have focused on utilizing facial shape priors such as
face landmarks and parsing maps. While these priors can provide facial geometric cues …

We propose a method to estimate 3D human poses from substantially blurred images. The
key idea is to tackle the inverse problem of image deblurring by modeling the forward …