13 April 2015 Blind image deblurring based on trained dictionary and curvelet using sparse representation
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Proceedings Volume 9522, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014, Part II; 95222G (2015) https://doi.org/10.1117/12.2181535
Event: Selected Proceedings of the Photoelectronic Technology Committee Conferences held August-October 2014, 2014, China, China
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.
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Liang Feng, Liang Feng, Qian Huang, Qian Huang, Tingfa Xu, Tingfa Xu, Shao Li, Shao Li, } "Blind image deblurring based on trained dictionary and curvelet using sparse representation", 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; https://doi.org/10.1117/12.2181535
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