›› 2018, Vol. 33 ›› Issue (3): 502-510.doi: 10.1007/s11390-018-1834-3

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2018 • Previous Articles     Next Articles

Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient

Guang-Hao Ma1, Ming-Li Zhang2, Xue-Mei Li3,*, Cai-Ming Zhang2,3,4   

  1. 1 School of Computer Science and Technology, Shandong University, Jinan 250101, China;
    2 Shandong Co-Innovation Center of Future Intelligent Computing, Yantai 264025, China;
    3 School of Software, Shandong University, Jinan 250101, China;
    4 Digital Media Research Institute, Shandong University of Finance and Economics, Jinan 250061, China
  • Received:2018-01-05 Revised:2018-03-30 Online:2018-05-05 Published:2018-05-05
  • Contact: Xue-Mei Li E-mail:xmli@sdu.edu.cn
  • About author:Guang-Hao Ma is currently a M.S. student in the School of Computer Science and Technology, Shandong University, Jinan. He received his B.S. degree in computer science from Shandong University, Weihai, in 2015. His research interests include image smoothing and computer vision.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61373078, 61572292, 61602277, and 61332015, the Key Project of National Natural Science Foundation of China Joint Fund with Zhejiang Integration of Informatization and Industrialization under Grant No. U1609218, and the Natural Science Foundation of Shandong Province of China under Grant No. ZR2016FQ12.

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Image smoothing is a crucial image processing topic in image processing and has wide applied backgrounds. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance in many situations because textures containing obvious edges and large gradient changes are easy to be preserved as the main edges. In this paper, we propose a novel framework for image smoothing combined with the constraint of sparse high frequency gradient for texture image. 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. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction and image composition.

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