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NLTV-Gabor-based models for image decomposition and denoising

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Abstract

By using the nonlocal total variation (NLTV) as the regularization and Gabor functions as the fidelity, this paper proposes two novel models for image decomposition and denoising. The presented models closely incorporate the advantages of the NLTV and Gabor wavelets-based methods. These improvements are aimed at overcoming the drawbacks of staircase artifacts and loss of edge details caused by the traditional variational frameworks. Furthermore, on the basis of Chambolle’s projection algorithm, we introduce two extremely efficient numerical methods to solve the resulting optimization problems. Finally, compared with several popular and powerful numerical methods, this article confirms the superiorities of the developed strategies for image decomposition and denoising in terms of visual quality and quantitative assessments.

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Authors and Affiliations

  1. School of Mathematics and Computational Science, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Xinwu Liu

  2. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Yue Chen

Authors
  1. Xinwu Liu
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  2. Yue Chen
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Correspondence to Xinwu Liu.

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This work was supported by National Natural Science Foundation of China (61402166) and Hunan Provincial Natural Science Foundation of China (14JJ3105).

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Liu, X., Chen, Y. NLTV-Gabor-based models for image decomposition and denoising. SIViP 14, 305–313 (2020). https://doi.org/10.1007/s11760-019-01558-6

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