Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

This article proposes a novel approach to regularize the ill-posed and non-linear blind
image deconvolution (blind deblurring) using deep generative networks as priors. We …

Many deep learning-based solutions to blind image deblurring estimate the blur
representation and reconstruct the target image from its blurry observation. However, these …

Most existing non-blind image deconvolution methods assume that the given blurring kernel
is error-free. In practice, blurring kernel often is estimated via some blind deblurring …

As handheld video cameras are now commonplace and available in every smartphone
images and videos can be recorded almost everywhere at any time. However, taking a quick …

In this paper, we present an end-to-end deblurring network designed specifically for a class
of data. Unlike the prior supervised deep-learning works that extensively rely on large sets of …

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

As an integral component of blind image deblurring, non-blind deconvolution removes
image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature …

Salient edge selection and time-varying regularization are two crucial techniques to
guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However …

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however
in the context of deep learning existing multi-scale algorithms not only require the use of …