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Frontiers of Optoelectronics

Front. Optoelectron.    2018, Vol. 11 Issue (3) : 267-274     https://doi.org/10.1007/s12200-018-0829-6
RESEARCH ARTICLE |
De-noising research on terahertz holographic reconstructed image based on weighted nuclear norm minimization method
Wenshu MA, Qi LI(), Jianye LU, Liyu SUN
National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150080, China
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Abstract

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 non-local 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.

Keywords terahertz digital holography      weighted nuclear norm minimization (WNNM)      non-local mean (NLM)     
Corresponding Authors: Qi LI   
Just Accepted Date: 09 July 2018   Online First Date: 30 July 2018    Issue Date: 31 August 2018
 Cite this article:   
Wenshu MA,Qi LI,Jianye LU, et al. De-noising research on terahertz holographic reconstructed image based on weighted nuclear norm minimization method[J]. Front. Optoelectron., 2018, 11(3): 267-274.
 URL:  
http://journal.hep.com.cn/foe/EN/10.1007/s12200-018-0829-6
http://journal.hep.com.cn/foe/EN/Y2018/V11/I3/267
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Wenshu MA
Qi LI
Jianye LU
Liyu SUN
Fig.1  Flow diagram of WNNM method
Fig.2  Flowchart of the NLM-WNNM method
Fig.3  Terahertz holographic reconstructed images. (a) Real image; (b) standard image; (c) histogram of the object regions in real image; (d) denoised by WNNM; (e) denoised by NLM; (f) denoised by BM3D; (g) denoised by NLM-WNNM
method PSNR SSIM
real image 64.52 0.7662
WNNM 64.90 0.8779
NLM 64.88 0.8789
BM3D 64.85 0.8812
NLM-WNNM 65.15 0.8866
Tab.1  PSNR and SSIM of terahertz image before and after denoised
Fig.4  Lena images. (a) Noise image; (b) standard image; (c) denoised by WNNM; (d) denoised by NLM; (e) denoised by NLM-WNNM; (f) denoised by BM3D
method PSNR SSIM
noise image 68.20 0.6188
WNNM 78.43 0.9021
NLM 78.34 0.8944
NLM-WNNM 78.81 0.9132
BM3D 80.10 0.9261
Tab.2  PSNR and SSIM of the Lena image before and after denoised
Fig.5  Comparison results. (a) Selected “hair” area; (b) columns 346−400 at row 77 comparison results; (c) rows 62−104 at column 372 comparison results
Fig.6  Comparison results. (a) Selected “eyeball” area; (b) columns 250−280 at row 271 comparison results; (c) rows 260−280 at column 269 comparison results
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