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Image smoothing based on global sparsity decomposition and a variable parameter

Computational Visual Media 2021年第4期7卷 483-497页

作者： Xiang Ma Xuemei Li Yuanfeng Zhou Caiming Zhang Shandong University Jinan 250101China Shandong Co-Innovation Center of Future Intelligent Computing Yantai 264025China Digital Media Technology Key Lab of Shandong Province Jinan 250014China

Smoothing images,especially with rich texture,is an important problem in computer *** an ideal result is difficult due to complexity,irregularity,and anisotropicity of the ***,some properties are shared by the texture and the structure in an *** is a hard compromise to retain structure and simultaneously remove *** create an ideal algorithm for image smoothing,we face three *** images with rich textures,the smoothing effect should be *** should overcome inconsistency of smoothing results in different parts of the *** is necessary to create a method to evaluate the smoothing *** apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems.A parametric surface constructed by an improved Bessel method is used to determine the smoothing *** evaluation measures:edge integrity rate,texture removal rate,and gradient value distribution are proposed to cope with the third *** use the alternating direction method of multipliers to complete the whole algorithm and obtain the *** show that our algorithm is better than existing algorithms both visually and *** also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

关键词： image smoothing texture removal global sparse decomposition Bessel method

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Image Smoothing Based on Image Decomposition and Sparse High Frequency Gradient

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Journal of Computer Science & Technology 2018年第3期33卷 502-510页

作者： Guang-Hao Ma Ming-Li Zhang Xue-Mei Li Cai-Ming Zhang School of Computer Science and Technology Shandong University Jinan 250101 China Shandong Co-Innovation Center of Future Intelligent Computing Yantai 264025 China School of Software Shandong University Jinan 250101 China Digital Media Research Institute Shandong University of Finance and Economics Jinan 250061 China

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.

关键词： image smoothing texture removal image decomposition

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