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Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient

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Abstract

Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance because texture containing obvious edges and large gradient changes is easy to be preserved as the main edges. In this paper, we propose a novel framework (DSHFG) for image smoothing combined with the constraint of sparse high frequency gradient for texture images. First, we decompose the image into two components: a smooth component (constant component) and a non-smooth (high frequency) component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art methods. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction, and image composition.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Guang-Hao Ma

  2. Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264025, China

    Ming-Li Zhang & Cai-Ming Zhang

  3. School of Software, Shandong University, Jinan, 250101, China

    Xue-Mei Li & Cai-Ming Zhang

  4. Digital Media Research Institute, Shandong University of Finance and Economics, Jinan, 250061, China

    Cai-Ming Zhang

Authors
  1. Guang-Hao Ma
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  2. Ming-Li Zhang
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  3. Xue-Mei Li
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  4. Cai-Ming Zhang
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Corresponding author

Correspondence to Xue-Mei Li.

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Ma, GH., Zhang, ML., Li, XM. et al. Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient. J. Comput. Sci. Technol. 33, 502–510 (2018). https://doi.org/10.1007/s11390-018-1834-3

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  • DOI: https://doi.org/10.1007/s11390-018-1834-3

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