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Proceedings Paper

Blind image deblurring based on trained dictionary and curvelet using sparse representation
Author(s): Liang Feng; Qian Huang; Tingfa Xu; Shao Li
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Paper Abstract

Motion blur is one of the most significant and common artifacts causing poor image quality in digital photography, in which many factors resulted. In imaging process, if the objects are moving quickly in the scene or the camera moves in the exposure interval, the image of the scene would blur along the direction of relative motion between the camera and the scene, e.g. camera shake, atmospheric turbulence. Recently, sparse representation model has been widely used in signal and image processing, which is an effective method to describe the natural images. In this article, a new deblurring approach based on sparse representation is proposed. An overcomplete dictionary learned from the trained image samples via the KSVD algorithm is designed to represent the latent image. The motion-blur kernel can be treated as a piece-wise smooth function in image domain, whose support is approximately a thin smooth curve, so we employed curvelet to represent the blur kernel. Both of overcomplete dictionary and curvelet system have high sparsity, which improves the robustness to the noise and more satisfies the observer's visual demand. With the two priors, we constructed restoration model of blurred images and succeeded to solve the optimization problem with the help of alternating minimization technique. The experiment results prove the method can preserve the texture of original images and suppress the ring artifacts effectively.

Paper Details

Date Published: 13 April 2015
PDF: 8 pages
Proc. SPIE 9522, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II, 95222G (13 April 2015); doi: 10.1117/12.2181535
Show Author Affiliations
Liang Feng, China North Vehicle Research Institute (China)
Qian Huang, Beijing Research Institute of Mechanical and Electrical Engineering (China)
Tingfa Xu, Beijing Institute of Technology (China)
Shao Li, China North Vehicle Research Institute (China)

Published in SPIE Proceedings Vol. 9522:
Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II
Xiangwan Du; Jennifer Liu; Dianyuan Fan; Jialing Le; Yueguang Lv; Jianquan Yao; Weimin Bao; Lijun Wang, Editor(s)

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