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 …

Numerous learning-based approaches to single image deblurring for camera and object
motion blurs have recently been proposed. To generalize such approaches to real-world …

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 …

This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …

In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …

This paper introduces a novel large dataset for video deblurring, video super-resolution and
studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges …

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …

Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision
problem as blurs arise not only from multiple object motions but also from camera shake …

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 …

Motion blur from camera shake is a major problem in videos captured by hand-held devices.
Unlike single-image deblurring, video-based approaches can take advantage of the …