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arXiv 2510.26296math.AP
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耦合局部与非局部全变分流的图像去斑方法

Coupling local and nonlocal total variation flow for image despeckling

Harbin Institute of Technology(哈尔滨工业大学);China Jiliang University(中国计量大学);Guangxi University(广西大学);Deutsches Elektronen-Synchrotron DESY(德国电子同步加速器)

Yi Ran, Zhichang Guo, Kehan Shi, Qirui Zhou, Jingfeng Shao, Martin Burger, Boying Wu

AI总结:

针对图像去斑中局部与非局部全变分流的优缺点互补特性,提出耦合模型,证明其弱解的存在唯一性及相关性质,验证核重缩放下的收敛性,凸显耦合机制的去斑优势。

摘要:

在图像去噪任务中,非局部方程可有效保留纹理但正则化效果较弱,局部方程去噪能力强却无法保护纹理。为融合两类方法的优势,本文研究一种用于图像去斑的局部-非局部耦合全变分流模型。我们证明了该方程弱解的存在性与唯一性,推导了弱解的等价形式、渐近行为等若干性质,还验证了在核重缩放条件下,该方程的弱解收敛于经典全变分流的弱解。通过与局部、非局部图像去斑模型的对比,凸显了耦合机制的重要性。

英文摘要:

Nonlocal equations effectively preserve textures but exhibit weak regularization effects in image denoising, whereas local equations offer strong denoising capabilities yet fail to protect textures. To integrate the advantages of both approaches, this paper investigates a coupled local-nonlocal total variation flow for image despeckling. We establish the existence and uniqueness of the weak solution for the proposed equation. Several properties, including the equivalent forms of the weak solution and its asymptotic behavior, are derived. Furthermore, we demonstrate that the weak solutions of the proposed equation converge to the weak solution of the classical total variation flow under kernel rescaling. The importance of coupling is highlighted through comparisons with local and nonlocal models for image despeckling.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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