A New Blind Deblurring Method via Hyper-laplacian Prior

A New Hyper-Laplacian Prior-Based Deconvolution Method for Single Image Deblurring

Motion blur is an ill-posed problem which has been addressed for a long time in digital photographing. It usually happens under the circumstance without enough surrounding light. Several methods have been proposed for this problem. However, the ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization-based methods have been proposed, ...

Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior

Typical blur from camera shake often deviates from the standard uniform convolutional script, in part because of problematic rotations which create greater blurring away from some unknown center point. Consequently, successful blind deconvolution requires the estimation of a spatially-varying or non-uniform blur operator. Using ideas from Bayesian inference and convex analysis, this paper deriv...

Learning a Discriminative Prior for Blind Image Deblurring

We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input i...

Blind image deblurring via coupled sparse representation

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learningbased method of estimating blur kernel under the ‘0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the b...

Blind image deblurring using elastic-net based rank prior