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Optimal filter based on scale-invariance generation of natural images

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Abstract

One of the most striking properties of natural image statistics is the scale invariance. Some earlier studies have assumed that the kurtosis of marginal band pass filter response to be constant throughout scales for a natural image. In our study, this assumption is loosened by adaptively estimating an optimal filter computation whose response distributions through scales have the least Kullback–Leibler divergence. The adaptive filter and its responses characterize the scale-invariance property more accurately and effectively and are further utilized to model the statistics scale-invariance prior in this paper. Extensive experiments on image super-resolution and de-noising manifest that the explored natural images scale-invariance prior model achieves significant performance improvements over the current state-of-the-art schemes.

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Acknowledgments

This work was supported in part by the Major State Basic Research Development Program of China (973 Program 2015CB351804), the National Natural Science Foundation of China under Grant Nos. 61272386 and 61100096, the National Science Council of the Republic of China under Grant Nos. 103-2917-I-564-058, and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2061978).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Feng Jiang, Liqiang Pan, Hongwei Guo & Debin Zhao

  2. Department of Electrical Engineering, National Cheng Kung University, Tainan City, Taiwan

    Bo-Wei Chen

  3. Department of Multimedia, Sungkyul University, Anyang City, Korea

    Seungmin Rho

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wen Ji

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  1. Feng Jiang
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  4. Wen Ji
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  6. Hongwei Guo
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  7. Debin Zhao
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Correspondence to Bo-Wei Chen.

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Jiang, F., Chen, BW., Rho, S. et al. Optimal filter based on scale-invariance generation of natural images. J Supercomput 72, 5–23 (2016). https://doi.org/10.1007/s11227-015-1398-8

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