| 基于的DTV图像去噪模型 |
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| 引用本文： | 庞志峰,张慧丽,史宝丽. 基于的DTV图像去噪模型[J]. 北京航空航天大学学报, 2019, 45(3): 464-471. DOI: 10.13700/j.bh.1001-5965.2018.0329 |
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| 作者姓名： | 庞志峰 张慧丽 史宝丽 |
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| 作者单位： | 河南大学 数学与统计学院, 开封 475004 |
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| 基金项目： | 国家"973"计划（2015CB856003）;国家自然科学基金（11401170,U1304610）;工程数学建模与分析湖南省重点实验室开放基金（长沙理工大学）;河南大学优青培育项目（yqpy20170062） |
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| 摘 要： | 针对纹理图像的去噪问题,通过分析全变分（TV）去噪模型与方向全变分（DTV）去噪模型,提出了一种具有鲁棒性的基于的DTV去噪模型。为了刻画图像中的不同结构特征,该模型中DTV正则项的指数p由图像的结构来确定在（0,2）中自适应地选取。由于该模型是含有可分性算子的非光滑优化问题,可用交替方向乘子法（ADMM）求解,并能保证算法的收敛性。数值实验结果表明:与其他经典模型相比,提出的模型取得了更高的峰值信噪比和结构相似度,在去除噪声的同时能有效保持图像的细节信息。
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| 关 键 词： | 交替方向乘子法(ADMM) 方向全变分(DTV)模型 图像去噪 bjhkhtdxxb-45-3-464-M14" > http://www.w3.org/1999/xlink" xlink:href=" bjhkhtdxxb-45-3-464-M14.jpg" />(拟)范数图像去噪 ROF模型 |
| 收稿时间： | 2018-06-04 |
Image denoising model based on lp directional total variation |
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| PANG Zhifeng,ZHANG Huili,SHI Baoli. Image denoising model based on lp directional total variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 464-471. DOI: 10.13700/j.bh.1001-5965.2018.0329 |
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| Authors: | PANG Zhifeng ZHANG Huili SHI Baoli |
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| Affiliation: | School of Mathematics and Statistics, Henan University, Kaifeng 475004, China |
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| Abstract: | For the problem of texture image denoising, by analyzing the advantages and disadvantages of the total variation (TV) denoising model and the directional total variation (DTV) denoising model, we propose a robust denoising model based on lp directional total variation. In the proposed model, in order to efficiently characterize the different structural features in the image, the exponential p in the edge adaptive directional total variation regularization term can be availably chosen in (0,2) based on the structure in the image. Since the proposed model is a non-smooth convex optimization with separable operator, it can be solved by using the alternating direction method of multipliers (ADMM). Then the convergence of the numerical method can be efficiently kept. Compared with other classic models, numerical implementations show that the proposed model can achieve higher peak signal-to-noise ratio and structural similarity, and can effectively retain image details while removing noise. |
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| Keywords: | alternating direction method of multipliers (ADMM) direction total variation (DTV) model image denoising lp-(quasi)norm image denoising ROF model |
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