Dual-Channel Contrast Prior for Blind Image Deblurring

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...

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 ...

Blind image deblurring using elastic-net based rank prior

Improving Dark Channel Prior for Single Image Dehazing

This paper proposes an improved dark channel prior for removing haze from images. Dark channel prior is an effective method for removing haze. Dark channel is an image in the same size as the hazy image which is obtained by dividing the RGB images into windows and for each window, the minimum of each R, G and B channels are calculated. Then again the minimum of these three values is calculated ...