We propose a simple yet effective L_0-regularized prior based on intensity and gradient for
text image deblurring. The proposed image prior is motivated by observing distinct …

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 …

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 …

Remote sensing images destri** and denoising are both classical problems, which have
attracted major research efforts separately. This letter shows that the two problems can be …

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 …

In this paper, we propose a novel design of image deblurring in the form of one-shot
convolution filtering that can directly convolve with naturally blurred images for restoration …

This work studies dynamic scene deblurring (DSD) of a single photograph, mainly motivated
by the very recent DeblurGAN method. It is discovered that training the generator alone of …

This paper addresses the problem of blind deblurring of single remote sensing (RS) images
with deep neural networks. Most existing deep learning-based methods are migrated from …

Image deblurring and its counterpart blind problem are undoubtedly two fundamental tasks
in computational imaging and computer vision. Interestingly, deterministic edge-preserving …

Motion blur is a common artifact in image processing, specifically in e‐health services, which
is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, ie, the …