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Blur kernel estimation using sparsity and local smoothness prior

[+] Author Affiliations
Bo Dong, Zhiguo Jiang, Haopeng Zhang

Beihang University, School of Astronautics, Image Processing Center, Beijing, China

Beijing Key Laboratory of Digital Media, Beijing, China

Yifan Wang

China Aeronautical Radio Electronics Research Institute, Shanghai, China

J. Electron. Imaging. 26(3), 033024 (Jun 15, 2017). doi:10.1117/1.JEI.26.3.033024
History: Received April 5, 2016; Accepted May 31, 2017
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Abstract.  Since blur kernel estimation is an ill-posed problem, it is essential that it be constrained by parametric image priors. However, the previous normalized sparsity measure alters the kernel structure during estimation. To address the problem of single-image blur kernel estimation, a local smoothness prior is introduced to the normalized sparsity model to constrain the blurred image gradient to be similar to the unblurred one. Moreover, based on the inequality constraints, a kernel optimization algorithm is proposed to weaken the noise. Experimental results show that the proposed method is robust against noise and is able to estimate a stable blur kernel. It outperforms other state-of-the-art methods on both synthetic and real data.

© 2017 SPIE and IS&T

Citation

Bo Dong ; Zhiguo Jiang ; Haopeng Zhang and Yifan Wang
"Blur kernel estimation using sparsity and local smoothness prior", J. Electron. Imaging. 26(3), 033024 (Jun 15, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033024


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