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| Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior |
| SUN Shijie①②③④ ZHAO Huaici①③④ LI Bo①②③④⑤ HAO Mingguo①③④ LÜ Jinfeng①②③④ |
①(Optical-Electronics Information Technology Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)
②(University of Chinese Academy of Sciences, Beijing 100049, China)
③(Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences, Shenyang 110016, China)
④(Key Laboratory of Image Understanding and Computer Vision, Shenyang 110016, China)
⑤(College of Information, Shenyang Institute of Engineering, Shenyang 110136, China) |
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Abstract The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such a task is severely ill-posed and even more challenging especially in the condition that the noise in the input image can not be negligible. In this paper, the main problem this study focuses on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating Maximum A Posteriori (MAP) estimation combined with low rank prior. First, when estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. Then the denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Finally, the non-blind deconvolution method is chosen to be used with sparse prior knowledge to achieve the final latent image restoration. Extensive experiments manifest the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.
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Received: 08 November 2016
Published: 11 May 2017
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| Fund: The Scientific Research Project of the Education Department of Liaoning Province (L2015368) |
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Corresponding Authors:
SUN Shijie
E-mail: sunshijie@sia.cn
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