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 …

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

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image
and the blur kernel given a blurry image. Recent progress on image deblurring always …

In this paper, we address the problem of estimating and removing non-uniform motion blur
from a single blurry image. We propose a deep learning approach to predicting the …

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature
of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but …

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-
restricted applications, but the quantum noise as resulted by the insufficient number of …

We propose a simple yet effective L 0-regularized prior based on intensity and gradient for
text image deblurring. The proposed image prior is based on distinctive properties of text …

Photographs taken in low-light conditions are often blurry as a result of camera shake, ie a
motion of the camera while its shutter is open. Most existing deblurring methods model the …

Ubiquitous image blur brings out a practically important question–what are effective features
to differentiate between blurred and unblurred image regions. We address it by studying a …

We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused
by defocus that spans a small number of pixels in images. This type of blur is common …