A Simple Local Minimal Intensity Prior and an Improved Algorithm for Blind Image Deblurring

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...

Kernel Optimization for Blind Motion Deblurring with Image Edge Prior

Image motion deblurring with unknown blur kernel is an ill-posed problem. This paper proposes a blind motion deblurring approach that solves blur kernel and the latent image robustly. For kernel optimization, an edge mask is used as an image prior to improve kernel update, then an edge selection mask is adopted to improve image update. In addition, an alternative iterative method is introduced ...

An improved non-blind image deblurring methods based on FoEs

Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction a...

Blind image deblurring using elastic-net based rank prior

Blind Image Deblurring Using Dark Channel Prior Supplemental Material

Overview In this supplemental material, we provide the proofs of Property 1 and Property 2 in Sections 1 and 2 respectively. We analyze the properties of the dark channel prior in image deblurring and demonstrate its effectiveness on natural images, text images and low-illumination images in Section 3. Section 4 provides detailed analysis of the proposed algorithm The algorithm details for solv...