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Sensors 2016, 16(6), 845; doi:10.3390/s16060845

Robust Image Restoration for Motion Blur of Image Sensors

1,2,3
,
1,2,* , 1,2
,
1,2,3
and
1,2,3
1
Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, China
2
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
3
University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100039, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 16 April 2016 / Revised: 24 May 2016 / Accepted: 31 May 2016 / Published: 9 June 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [7130 KB, uploaded 9 June 2016]   |  

Abstract

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively. View Full-Text
Keywords: blind image restoration; blur kernel estimation; edge selection; image sensor signal processing; motion deblurring blind image restoration; blur kernel estimation; edge selection; image sensor signal processing; motion deblurring
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, F.; Huang, Y.; Luo, Y.; Li, L.; Li, H. Robust Image Restoration for Motion Blur of Image Sensors. Sensors 2016, 16, 845.

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