Structure–texture image decomposition using a new non-local TV-Hilbert model

Lv Yehu

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Structure–texture image decomposition using a new non-local TV-Hilbert model

机译：结构纹理图像分解使用新的非本地TV-Hilbert模型

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Combining the advantages of the non-local total variation (TV) and the Gabor function, a new Gabor function based non-local TV-Hilbert model is presented to separate the structure and texture components of the image. Computationally, by introducing the dual form of the non-local TV, the authors reformulate the non-local TV-Hilbert minimisation problem into a convex-concave saddle-point problem. In the aspect of solving algorithm, by transforming the Chambolle-Pock's first-order primal-dual algorithm into a different equivalent form. The authors propose a proximal-based primal-dual algorithm to solve the convex-concave saddle-point problem. At last, experimental results demonstrate that the proposed new model outperforms several existing state-of-the-art variational models.

机译：结合非局部总变化（TV）和Gabor功能的优点，提出了一种新的Gabor功能的非本地TV-Hilbert模型，以分离图像的结构和纹理组件。通过介绍非本地电视的双重形式，作者将非本地TV-Hilbert最小化问题重构为凸凹鞍点问题。在求解算法的方面，通过将Chambolle-Pock的一阶原始算法转换为不同的等效形式。作者提出了一种基于近端的原始双向算法来解决凸凹鞍点问题。最后，实验结果表明，所提出的新模型优于几种现有的最先进的变分模型。

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来源
《Image Processing, IET》 |2020年第11期|2525-2531|共7页
作者
Lv Yehu;
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作者单位

Hebei Univ Technol Inst Math Tianjin 300401 Peoples R China;

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正文语种 eng
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关键词
variational techniques; image texture; convex programming; minimisation; image denoising; proximal-based primal-dual algorithm; Chambolle-Pock's first-order primal-dual algorithm; convex-concave saddle-point problem; nonlocal TV-Hilbert minimisation problem; texture components; Gabor function; nonlocal total variation; nonlocal TV-Hilbert model; structure-texture image decomposition;

机译：变分技术;图像纹理;凸编程;最小化;图像去噪;基于近端的原始 - 双重算法;Chambolle-Pock的一阶原始算法;凸凹马鞍点问题;非本体电视 - 希尔伯特最小化问题;纹理组件;Gabor功能;非局部总变异;非本体电视 - 希尔伯特模型;结构纹理图像分解;

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1. Properties of $BV-G$ Structures $+$ Textures Decomposition Models. Application to Road Detection in Satellite Images [J] . Gilles J.Meyer Y. Image Processing, IEEE Transactions on . 2010,第11期

机译：$ BV-G $结构$ + $纹理分解模型的属性。在卫星图像道路检测中的应用
2. Structure-texture image decomposition - Modeling, algorithms, and parameter selection [J] . Aujol JF, Gilboa G, Chan T, International Journal of Computer Vision . 2006,第1期

机译：结构纹理图像分解-建模，算法和参数选择
3. Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions [J] . Liu A., Lin W., Paul M., Circuits and Systems for Video Technology, IEEE Transactions on . 2010,第11期

机译：带有分离边缘和纹理区域的分解模型的图像的明显差异
4. Intrinsic Image Decomposition with Non-Local Texture Cues [C] . Li Shen, Ping Tan, Stephen Lin IEEE Conference on Computer Vision and Pattern Recognition . 2008

机译：与非局部纹理线索的内在图像分解
5. Unsupervised segmentation of noisy and textured images modeled by Markov random fields. [D] . Gregoriou, Georghios Kyriacos. 1992

机译：由Markov随机场建模的噪点和纹理图像的无监督分割。
6. Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning [O] . Niloofar Yousefi Moteghaed, Ahmad Mostaar, Keivan Maghooli, 2020

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7. Intrinsic image decomposition with non-local texture cues [O] . Li Shen, Ping Tan, Stephen Lin 2008

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8. Using Roughness Textures in Target Modelling - Impact on ISAR Images Calculations of the ZSU 23-4 [R] . Mametsa, H. J. , Berges, A. , Latger, J. 2005

机译：在目标建模中使用粗糙度纹理 - 对Iss图像的影响ZsU 23-4的计算

Structure–texture image decomposition using a new non-local TV-Hilbert model

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