De-noising research on terahertz holographic reconstructed image based on weighted nuclear norm minimization method
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Terahertz imaging is one of the forefront topics of imaging technology today. Denoising process is the key for improving the resolution of the terahertz holographic reconstructed image. Based on the fact that the weighted nuclear norm minimization (WNNM) method preserves the details of the reconstructed image well and the nonlocal mean (NLM) algorithm performs better in the removal of background noise, this paper proposes a new method in which the NLM algorithm is used to improve the WNNM method. The experimental observation and quantitative analysis of the denoising results prove that the new method has better denoising effect for the terahertz holographic reconstructed image.
Keywordsterahertz digital holography weighted nuclear norm minimization (WNNM) non-local mean (NLM)
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This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 61377110).
- 1.Zheng X, Wang X, Sun W, Feng S, Ye J, Zhang Y. Developments and applications of the terahertz digital holography. Chinese Journal of Lasers, 2014, 41(2): 1–11Google Scholar
- 2.Wang D, Huang H, Zhou X, Rong L, Li Z, Lin Q, Wang Y. Phasecontrast Imaging by the continuous-wave terahertz in-line digital holography. Chinese Journal of Lasers, 2014, 41(8): 08090031–08090036Google Scholar
- 4.Cui S S, Li Q. A comparison of filtering techniques on denoising terahertz coaxial digital holography image. SPIE, 2016, 10157: 101571R1–101571R5Google Scholar
- 9.Gu S H, Zhang L, Zuo W M, Feng X C. Weighted nuclear norm minimization with application to image denoising. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA: IEEE, 2014, 2862–2869Google Scholar
- 11.Sun S. Image denoising via weighted nuclear norm minimization and Gaussian mixed model. Jisuanji Yingyong, 2017, 37(5): 1471–1474Google Scholar
- 12.Buades A, Bartomeu C A, Morel J M. A non-local algorithm for image denoising. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). San Diego, CA, USA: IEEE, 2005, 61–65Google Scholar
- 13.Buades A, Bartomeu C A, Morel J M. Non-local means denoising. IPOL Journal—Image Processing on Line, 2011, 1: 208–212Google Scholar