Removing blur kernel noise via a hybrid ℓp norm

Xin Yu; Shunli Zhang; Xiaolin Zhao; Li Zhang

doi:10.1117/1.JEI.24.1.013011

9 January 2015 Removing blur kernel noise via a hybrid ℓp norm

Xin Yu, Shunli Zhang, Xiaolin Zhao, Li Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013011 (January 2015). https://doi.org/10.1117/1.JEI.24.1.013011

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 ℓ_p norm, which is composed of ℓ₂ norm and ℓ_p norm with 0.7≤p<1, to construct a sparsity constraint. Using the hybrid ℓ_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.

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Xin Yu, Shunli Zhang, Xiaolin Zhao, and Li Zhang "Removing blur kernel noise via a hybrid ℓp norm," Journal of Electronic Imaging 24(1), 013011 (9 January 2015). https://doi.org/10.1117/1.JEI.24.1.013011

Published: 9 January 2015

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