Abstract
A new methodology for blur detection with multi-method fusion is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. We try to discover the underlying performance complementary patterns of several state-of-the-art methods, then use the pattern specific to each image to get a better overall result. Specifically, a Conditional Random Filed (CRF) framework is adopted for multi-method blur detection that not only models the contribution from individual blur detection result but also the interrelation between neighbouring pixels. Considering the dependence of multi-method fusion on the specific image, we single out a subset of images similar to the input image from a training dataset and train the CRF-based multi-method fusion model only using this subset instead of the whole training dataset. The proposed multi-method fusion approach is shown to stably outperform each individual blur detection method on public blur detection benchmarks.
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Acknowledgement
This work was supported in part by the National Science Foundation of China No. 61472103, and Key Program Grant of National Science Foundation of China No. 61133003.
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Huang, Y., Yao, H., Zhao, S. (2015). Blur Detection Using Multi-method Fusion. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_37
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DOI: https://doi.org/10.1007/978-3-319-24075-6_37
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