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A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit

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Abstract

The orthogonal matching pursuit algorithm directly samples the image signal by using the sparsity of the image signal. It uses the atom that matches the image signal feature to describe the image, which can better preserve the detailed features of the image. In this paper, an improvement of variable step size and optimized cut-off conditions is made. The experimental results show that the improved algorithm makes the denoised image clearer and have more detailed features.

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Acknowledgements

This work is supported by Qing Lan Project of Jiangsu Province-China (Grant No. 2017-AD41779), the Fundamental Research Funds for the Central Universities-China (Grant No. 30916011206) and the Six Talent Peaks Project in Jiangsu Province-China (Grant No. 2015-XCL-008).

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Authors and Affiliations

  1. Department of Optoelectronic Technology, School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zhuang Zhang, Xu Chen, Lei Liu, Yefei Li & Yubin Deng

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  1. Zhuang Zhang
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  2. Xu Chen
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  3. Lei Liu
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  4. Yefei Li
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  5. Yubin Deng
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Correspondence to Lei Liu.

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Zhang, Z., Chen, X., Liu, L. et al. A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit. SIViP 14, 737–745 (2020). https://doi.org/10.1007/s11760-019-01606-1

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  • DOI: https://doi.org/10.1007/s11760-019-01606-1

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