Blind Image Deblurring using the l0 Gradient Prior

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...

L0-Regularized Intensity and Gradient Prior for Text Images Deblurring and Beyond Supplemental Material

1 QUANTITATIVE EVALUATION ON TEXT IMAGE DATASET In this section, we have created a dataset containing 15 images and 8 blur kernels extracted from Levin et al. [1]. Similar to [1], we can generate 120 different blurred images. The 15 ground truth images and 8 blur kernels are shown in Figures 1 and 2, respectively. For simplicity. we still use L0RIG and IL0RIG to denote the original text deblurr...

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 ...