Neural networks (NNs) are becoming the tool of choice for sharpening blurred images. We
discuss and categorize deblurring NNs. Then we evaluate seven NNs for non-blind …

Estimating blur kernels from real world images is a challenging problem as the linear image
formation assumption does not hold when significant outliers, such as saturated pixels and …

Blind image deconvolution (BID) is about recovering a latent image with sharp details from
its blurred observation generated by the convolution with an unknown smoothing kernel …

Blind image deblurring is a challenging problem in computer vision and image processing.
In this paper, we propose a new l 0-regularized approach to estimate a blur kernel from a …

In this study, we propose a patch-wise maximum gradient (PMG) prior for effective blind
image deblurring. Our work is motivated by the fact that the maximum gradient values of non …

Blind motion deblurring is an important problem that receives enduring attention in last
decade. Based on the observation that a good intermediate estimate of latent image for …

Blind image deblurring is a challenging image processing problem, and a proper solution for
this problem has many applications in the real world. This is an ill-posed problem, as both …

Deep learning has been a powerful tool for solving many inverse imaging problems. The
majority of existing deep-learning-based solutions are supervised on an external dataset …

Non-uniform blind deblurring of dynamic scenes has always been a challenging problem in
image processing because of the diverse of blurring sources. Traditional methods based on …

Recovering clear images from blurry ones with an unknown blur kernel is a challenging
problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a …