Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior

SUN Shijie; ZHAO Huaici; LI Bo; HAO Mingguo; Lü Jinfeng

doi:10.11999/JEIT161206

Volume 39 Issue 8

Aug. 2017

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SUN Shijie, ZHAO Huaici, LI Bo, HAO Mingguo, Lü Jinfeng. Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1919-1926. doi: 10.11999/JEIT161206

Citation:

SUN Shijie, ZHAO Huaici, LI Bo, HAO Mingguo, Lü Jinfeng. Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1919-1926. doi: 10.11999/JEIT161206

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Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior

doi: 10.11999/JEIT161206

Funds:

The Scientific Research Project of the Education Department of Liaoning Province (L2015368)

Received Date: 2016-11-08
Rev Recd Date: 2017-04-01
Publish Date: 2017-08-19

Abstract

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.
- Blind deconvolution,
- Maximum A Posteriori (MAP) estimation,
- Noisy and blurry image,
- Low rank prior

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References

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