Abstract
Blur measurement of partially blurred image is still far from being resolved. This calls for more distinctive blur features and, even more importantly, a global refinement strategy that has not been considered by existing studies. In this paper we propose a new spatial and frequencial coupled blur descriptor by composing the number of extreme points, the vector of all singular values and the entropy-weighted pooling of the high frequency DCT coefficients. We also introduce a global refinement scheme to explore the merits of saliency for further refining the initial measurements. Consequently, we propose a novel saliency constrained blur measurement method by integrating a neural network based blur metric and a superpixel-scale blur refinement together. Experimental results show the efficiency of our method qualitatively and quantitatively, especially for the images with flat textures.
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Acknowledgement
This work is co-supported by Key Science & Technology Program of Anhui Province (1604d0802004), Nature Science Foundation of China (61502005) and Nature Science Foundation of Anhui Province (1608085QF129, 1708085MF151).
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Fang, X., Guo, Q., Ding, C., Wang, L., Deng, Z. (2018). Blur Measurement for Partially Blurred Images with Saliency Constrained Global Refinement. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_31
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DOI: https://doi.org/10.1007/978-3-030-00764-5_31
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