A Novel Blind Motion Blur Restoration Algorithm for Text Images

WANG Simin; LI Xiaoguang; ZHANG Hui; ZHUO Li

doi:10.1049/cje.2019.12.001

Volume 29 Issue 1

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WANG Simin, LI Xiaoguang, ZHANG Hui, et al., “A Novel Blind Motion Blur Restoration Algorithm for Text Images,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 161-167, 2020, doi: 10.1049/cje.2019.12.001

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WANG Simin, LI Xiaoguang, ZHANG Hui, et al., “A Novel Blind Motion Blur Restoration Algorithm for Text Images,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 161-167, 2020, doi: 10.1049/cje.2019.12.001

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A Novel Blind Motion Blur Restoration Algorithm for Text Images

doi: 10.1049/cje.2019.12.001

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61471013, No.61531006, No.61602018, No.61701011), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No.CIT&TCD20150311), and the RiXin Talent Project of Beijing University of Technology (No.2014-RX-14).

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Corresponding author: LI Xiaoguang (corresponding author) was born in 1980. He received the Ph.D. degree in circuit and system from Beijing University of Technology, China in 2008. He is an associate professor of electronic science and technology in Beijing University of Technology. His research interests include image super resolution and restoration. (Email:lxg@bjut.edu.cn)
Received Date: 2018-08-18
Rev Recd Date: 2018-09-26
Publish Date: 2020-01-10

Abstract

Abstract

Text images captured by the surveillance system or hand-hold cameras often suffer from motion blur due to the complex relative motion between the camera and the target during the exposure time. The accuracy of the kernel estimation and the effective priors for clear text images are two important keys in blind motion deblurring. A novel blind motion deblurring algorithm is proposed for text images in a complex scene. A criterion for selecting informative edges and an L₀ constraint are combined to improve the accuracy of the kernel estimation. Then, a fast non-blind deconvolution scheme is applied to accelerate the algorithm. Experimental results on text images show that the proposed method can achieve high-quality results with low computational complexity.
- Text image deblurring,
- Informative edges selection,
- L₀-Regularized prior,
- Blind motion restoration

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References(17)

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