We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

Dynamic Scene deblurring is a challenging low-level vision task where spatially variant blur
is caused by many factors, eg, camera shake and object motion. Recent study has made …

This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …

Just like many other topics in computer vision, image classification has achieved significant
progress recently by using deep learning neural networks, especially the Convolutional …

Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel
is unknown. To solve this ill-posed problem, a great amount of image priors have been …

We present an effective semi-supervised learning algorithm for single image dehazing. The
proposed algorithm applies a deep Convolutional Neural Network (CNN) containing a …

Video deblurring is a challenging task due to the spatially variant blur caused by camera
shake, object motions, and depth variations, etc. Existing methods usually estimate optical …

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high-and low-
resolution images, achieve state-of-the-art performance in single-image super-resolution …

Image deblurring aims to restore the latent sharp images from the corresponding blurred
ones. In this paper, we present an unsupervised method for domain-specific, single-image …

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art
performances for many computer vision tasks, including classification and super-resolution …