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Journal of Electronic Imaging

Removing blur kernel noise via a hybrid ℓp norm
Author(s): Xin Yu; Shunli Zhang; Xiaolin Zhao; Li Zhang
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Paper Abstract

When estimating a sharp image from a blurred one, blur kernel noise often leads to inaccurate recovery. We develop an effective method to estimate a blur kernel which is able to remove kernel noise and prevent the production of an overly sparse kernel. Our method is based on an iterative framework which alternatingly recovers the sharp image and estimates the blur kernel. In the image recovery step, we utilize the total variation (TV) regularization to recover latent images. In solving TV regularization, we propose a new criterion which adaptively terminates the iterations before convergence. While improving the efficiency, the quality of the final results is not degraded. In the kernel estimation step, we develop a metric to measure the usefulness of image edges, by which we can reduce the ambiguity of kernel estimation caused by small-scale edges. We also propose a hybrid p norm, which is composed of 2 norm and p norm with 0.7≤p<1, to construct a sparsity constraint. Using the hybrid p norm, we reduce a wider range of kernel noise and recover a more accurate blur kernel. The experiments show that the proposed method achieves promising results on both synthetic and real images.

Paper Details

Date Published: 9 January 2015
PDF: 19 pages
J. Electron. Imaging. 24(1) 013011 doi: 10.1117/1.JEI.24.1.013011
Published in: Journal of Electronic Imaging Volume 24, Issue 1
Show Author Affiliations
Xin Yu, Tsinghua Univ. (China)
Shunli Zhang, Tsinghua Univ. (China)
Xiaolin Zhao, Air Force Engineering Univ. (China)
Li Zhang, Tsinghua Univ. (China)


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