小结构的分段梯度先验和保持对比度的图像平滑

IF 3.4 2区 数学 Q1 MATHEMATICS, APPLIED
Tingting Li , Fang Li , Huiqing Qi
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引用次数: 0

摘要

图像平滑是数字图像处理中的一项基本任务,有着广泛的应用。然而,传统的纹理平滑技术往往会导致小的结构信息和对比度的丢失或模糊。在本文中,我们引入了一种分段梯度先验来克服这一缺点。先验算法基于四段分段惩罚函数,可以处理不同尺度的信号。我们还提出了一种有效的基于乘法器框架的交替方向迭代算法,并提供了该算法全局收敛性的理论证明。我们的方法在各种应用中显示出有希望的结果,包括纹理去除,剪贴画压缩伪影去除和边缘检测。实验结果证明了该方法在这些应用中的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A piecewise gradient prior for small structures and contrast preserving image smoothing
Image smoothing is a fundamental task in digital image processing with broad applications. However, traditional texture smoothing techniques often result in the loss or blurring of small structural information and contrast. In this paper, we introduce a piecewise gradient prior aimed at overcoming this drawback. The prior is based on a four-segment piecewise (FSP) penalty function, which can process signals at different scales. We also present an effective iterative algorithm based on the alternate direction method of multipliers framework and provide theoretical proof of global convergence for the proposed algorithm. Our method has shown promising results in various applications, including texture removal, clip art compression artifact removal, and edge detection. Experimental results demonstrate the effectiveness and superior performance of our approach in these applications.
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来源期刊
CiteScore
7.90
自引率
10.00%
发文量
755
审稿时长
36 days
期刊介绍: Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.
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