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Kernel estimation for robust motion deblurring of noisy and blurry images

[+] Author Affiliations
Shijie Sun, Jinfeng Lv

Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

University of Chinese Academy of Science, No. 19, Yuquan Road, Shijingshan District, Beijing City 100049, China

Chinese Academy of Sciences, Key Laboratory of Optical-Electronics Information Processing, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Key Laboratory of Image Understanding and Computer Vision, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Huaici Zhao, Mingguo Hao

Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Chinese Academy of Sciences, Key Laboratory of Optical-Electronics Information Processing, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Key Laboratory of Image Understanding and Computer Vision, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Bo Li

Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

University of Chinese Academy of Science, No. 19, Yuquan Road, Shijingshan District, Beijing City 100049, China

Chinese Academy of Sciences, Key Laboratory of Optical-Electronics Information Processing, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Key Laboratory of Image Understanding and Computer Vision, No. 114, Nanta Street, Shenhe District, Shenyang City 110016, China

Shenyang Institute of Engineering, College of Information, No. 18, Puchang Road, Shenbei New Area, Shenyang City 110136, China

J. Electron. Imaging. 25(3), 033019 (Jun 16, 2016). doi:10.1117/1.JEI.25.3.033019
History: Received February 26, 2016; Accepted May 26, 2016
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Abstract.  Most state-of-the-art single image blind deblurring techniques are still sensitive to image noise, leading to serious performance degradation in their blur kernel estimation when the input image noise increases. We found that reliable kernel estimation could not be given by directly using denoising and existing deblurring algorithms in many cases. We focus on how to estimate a good blur kernel from a noisy blurred image via using the image structure. First, we applied denoising as a preprocess to remove the input image noise and then computed salient image structure of the denoised result based on the total variation model. We also applied a gradient selection method to remove those salient edges that have a possible adverse effect on blur kernel estimation. Next, we adopted a two-phase estimation strategy to obtain higher quality blur kernel estimation by jointly applying kernel estimation from salient image structure and iterative support detection (ISD) kernel refinement. Finally, we used the nonblind deconvolution method based on sparse prior knowledge to restore the latent image. Extensive experiments testify to the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.

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© 2016 SPIE and IS&T

Citation

Shijie Sun ; Huaici Zhao ; Bo Li ; Mingguo Hao and Jinfeng Lv
"Kernel estimation for robust motion deblurring of noisy and blurry images", J. Electron. Imaging. 25(3), 033019 (Jun 16, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.3.033019


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