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Segmenting, removing and ranking partial blur

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Abstract

Image blur is a common phenomenon in daily life. Due to the great challenge, image restoration fascinates researchers to find out the solutions. Considering different types of blur, we propose a framework to segment the partial blur from a single image and then restore the latent information. In general, some morphological technologies are applied to separate the blur area. Traditionally, blind deconvolution method is applied in underdetermined conditions. In this research, we marginalize the kernel estimation by separating the problem into two stages, both of which are combined with different useful priors. A criterion of ranking the blur degree of a partial blur image is also proposed at the end of this paper. Experimental results demonstrate the accuracy and superiority of our approach.

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Acknowledgments

Dr. Bo Tao owes a great deal for his support in paper revising. We would also like to thank the anonymous reviewers for their helpful feedback.

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Correspondence to Wei Wang.

Additional information

Wang proposed and implemented the framework of this research, and he put forward the technological innovation of the key points under the guidance of Zheng; Zheng also accelerated the mathematical process in implementation; Zhou assisted in implementation and provided the experimental hardware platform. This work was supported by NSFC-CAS Joint Fund (No. U1332130), 111 Projects (No. B07033), 973 Project (No. 2014CB931804).

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Wang, W., Zheng, Jj. & Zhou, Hj. Segmenting, removing and ranking partial blur. SIViP 8, 647–655 (2014). https://doi.org/10.1007/s11760-013-0573-8

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  • DOI: https://doi.org/10.1007/s11760-013-0573-8

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