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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

image smoothing based on global sparsity decomposition and a variable parameter

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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

image smoothing of multispectral imagery based on the HNN and geo-statistics

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遥感学报 2011年第3期15卷 640-644页

作者： Nguyen Quang Minh Department of Surveying and Mapping Deputy Head of International Cooperation OfficeUniversity of Mining and Geology

A new method for image down-scaling using geostatistical interpolation or smoothing based on the Hopfield Neural Network(HNN) and zero semivariance value is *** method utilises the smoothing effect of the semivariogram matching process to produce the smoothened sub-pixel multispectral(MS) image with smaller RMSEs in comparison with the bilinear *** fact,the zero semivariograms increase the spatial correlation between the adjacent sub-pixels of the superresolution *** higher spatial correlation,the resulting super-resolution MS image has smaller RMSEs compared with the original coarse image.

关键词： image smoothing HNN Geostistics

A nonlocal gradient concentration method for image smoothing

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Computational Visual Media 2015年第3期1卷 197-209页

作者： Qian Liu Caiming Zhang Qiang Guo Yuanfeng Zhou School of Computer Science and Technology Shandong University Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology

It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.

关键词： image smoothing nonlocal similarity L0 norm edge detection

Performance Analysis of image smoothing Techniques on a New Fractional Convolution Mask for image Edge Detection

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Open Journal of Applied Sciences 2016年第7期6卷 478-488页

作者： Peter Amoako-Yirenkyi Justice Kwame Appati Isaac Kwame Dontwi Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana National Institute of Mathematical Sciences Kumasi Ghana

We present the analysis of three independent and most widely used image smoothing techniques on a new fractional based convolution edge detector originally constructed by same authors for image edge analysis. The implementation was done using only Gaussian function as its smoothing function based on predefined assumptions and therefore did not scale well for some types of edges and noise. The experiments conducted on this mask using known images with realistic geometry suggested the need for image smoothing adaptation to obtain a more optimal performance. In this paper, we use the structural similarity index measure and show that the adaptation technique for choosing smoothing function has significant advantages over a single function implementation. The new adaptive fractional based convolution mask can smoothly find edges of various types in detail quite significantly. The method can now trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges.

关键词： Cubic B-Spline Edge Detection Fractional Edge Gaussian Filter image smoothing Median Filter Structural Similarity Index Measure

Improved image filter based on SPCNN

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Science in China(Series F) 2008年第12期51卷 2115-2125页

作者： ZHANG YuDong WU LeNan School of Information Science & Engineering Southeast University Nanjing 210096 China

By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.

关键词： Nagao filter pulse coupled neural network image smoothing image de-noising salt and pepper noise,edge preserving

Least-squares images for edge-preserving smoothing

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Computational Visual Media 2015年第1期1卷 27-35页

作者： Hui Wang Junjie Cao Xiuping Liu Jianmin Wang Tongrang Fan Jianping Hu School of Information Science and Technology Shijiazhuang Tiedao University School of Mathematical Sciences Dalian University of Technology School of Sciences Northeast Dianli University

In this paper, we propose least-squares images(LS-images) as a basis for a novel edgepreserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-theart works. We also show diverse applications of LSimages, such as detail manipulation, edge enhancement,and clip-art JPEG artifact removal.

关键词： feature-preserving image enhancement image smoothing least-squares images(LS-images)

An Improved RANSAC Algorithm of Color image Stitching

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An Improved RANSAC Algorithm of Color Image Stitching