? 基于图像分解和稀疏高频梯度的图像平滑
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (3) :502-510    DOI: 10.1007/s11390-018-1834-3
Special Section of CVM 2018 << Previous Articles | Next Articles >>
基于图像分解和稀疏高频梯度的图像平滑
Guang-Hao Ma1, Ming-Li Zhang2, Xue-Mei Li3,*, Cai-Ming Zhang2,3,4
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
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 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

摘要
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摘要 图像平滑是图像处理中的重要研究问题,有广泛的应用背景。对于纹理丰富的图像,已有的图像平滑方法在很多情况下难以取得明显的纹理去除效果,因为纹理显著的部分往往含有较明显的边缘与较大的梯度变化,容易被当作边缘保留下来。本文提出了一种新的图像平滑框架,结合稀疏高频梯度约束来处理纹理图像。首先将图像分解成平滑部分和非平滑部分,然后去掉含有高频梯度的非平滑部分,利用稀疏高频梯度的约束对其它部分进行图像平滑。实验结果表明,本文方法与最新的方法相比对纹理的去除效果明显提高。此外我们的方法有着多种应用,比如图像的边缘检测、细节的增强、图像的抽象化以及图像的合成。
关键词图像平滑   纹理去除   图像分解     
Abstract: 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.
Keywordsimage smoothing   texture removal   image decomposition     
Received 2018-01-05;
本文基金:

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.

通讯作者: Xue-Mei Li     Email: 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.
引用本文:   
Guang-Hao Ma, Ming-Li Zhang, Xue-Mei Li, Cai-Ming Zhang.基于图像分解和稀疏高频梯度的图像平滑[J]  Journal of Computer Science and Technology , 2018,V33(3): 502-510
Guang-Hao Ma, Ming-Li Zhang, Xue-Mei Li, Cai-Ming Zhang.Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient[J]  Journal of Computer Science and Technology, 2018,V33(3): 502-510
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http://jcst.ict.ac.cn:8080/jcst/CN/10.1007/s11390-018-1834-3
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