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Automatic blur region segmentation approach using image matting

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Abstract

For images with partial blur such as local defocus or local motion, deconvolution with just a single point spread function surely could not restore the images correctly. Thus, restoration relying on blur region segmentation is developed widely. In this paper, we propose an automatic approach for blur region extraction. Firstly, the image is divided into patches. Then, the patches are marked by three blur features: gradient histogram span, local mean square error map, and maximum saturation. The combination of three measures is employed as the initialization of iterative image matting algorithm. At last, we separate the blurred and non-blurred region through the binarization of alpha matting map. Experiments with a set of natural images prove the advantage of our algorithm.

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Acknowledgments

We wish to thank the reviewers for their comments and suggestions which have helped improve the content of the paper. And we thank Dr. Li for checking the text. This research is supported by the National Basic Research Program (973) of China (Grant No. 2009CB724006) and the National Hi-Tech Research and Development Program (863) of China (2009AA12Z108).

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Authors and Affiliations

  1. State Key Laboratory of Optical Instrumentation, Zhejiang University, Hangzhou, 310027, China

    Jufeng Zhao, Huajun Feng, Zhihai Xu, Qi Li & Xiaoping Tao

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  1. Jufeng Zhao
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  2. Huajun Feng
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  3. Zhihai Xu
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  4. Qi Li
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  5. Xiaoping Tao
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Correspondence to Huajun Feng.

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Zhao, J., Feng, H., Xu, Z. et al. Automatic blur region segmentation approach using image matting. SIViP 7, 1173–1181 (2013). https://doi.org/10.1007/s11760-012-0381-6

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