Abstract
The motion blurred image is caused by the relative motion between the target and the capturing device during the exposure time. It’s difficult to analyze the face information of the motion blurred face image, therefore motion deblurring is needed. However, the existing algorithms cannot deal with the diversity of motion blur kernels well. Based on that, this paper proposes an iterative spiral optimization algorithm for blind motion blurring. The algorithm makes the blurred image spirally approximate the sharp image by calling the deblurring generator multiple times. It is proved that the algorithm can effectively restore the motion blurred image with diverse blurred kernels in the approximate natural state, and improve the visual effect of the image.
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Acknowledgments
This research was financially supported by the Science and Technology Foundation of Henan Province of China (182102210302), Research Foundation for Talented Scholars of Henan Institute of Science and Technology, National Natural Science Foundation of China (61703143), Science and Technology Project of Henan Province (192102310260), Scientific and Technological Innovation Talents in Xinxiang (CXRC17004), young backbone teacher training project of Henan University (2017GGJS123), and Science and Technology Major Special Project of Xinxiang City (ZD18006).
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Ma, Y., Xu, Y., Wu, L., Xu, T., Zhao, X., Cai, L. (2019). Face Image Deblurring Based on Iterative Spiral Optimazation. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_18
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DOI: https://doi.org/10.1007/978-3-030-31456-9_18
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