Adaptive Iterative Denoising of Terahertz Image Based on Noise Estimation

Fig. 12. Iterative denoising results of Fuwa image. (a) Image standard deviation; (b) noise standard deviation; (c) 2nd iteration; (d) 3rd iteration; (e) 4th iteration; (f) 5th iteration

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Fig. 13. Denoising result of Fuwa image. (a) BNLM denoising; (b) NLM denoising

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Table 1. Comparison of R_PSNR and M_MSSIM results of denoising images with different parameters

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Table 1. Comparison of R_PSNR and M_MSSIM results of denoising images with different parameters

Experimental parameter		R_PSNR	M_MSSIM
	0.85	16.774	0.8853
	0.8	16.774	0.8873
T(l=3)	0.5	16.714	0.8873
	0.3	16.694	0.8869
	0.15	16.683	0.8868
	0.85	16.788	0.8867
	0.8	16.789	0.8876
T(l=4)	0.5	16.789	0.8865
	0.3	16.768	0.8869
	0.15	16.718	0.8868
	3	16.781	0.8787
	4	16.788	0.8892
t	5	16.750	0.8892
	6	16.715	0.8867
	7	16.685	0.8868
	1	16.987	0.8894
	2	16.870	0.8889
f	3	16.788	0.8867
	4	16.732	0.8822
	5	16.686	0.8779
	1.4	17.056	0.8845
	2.2	17.082	0.8897
h	2.3	17.082	0.8913
	2.4	17.077	0.8885
	3.2	16.987	0.8842

Table 2. Noise estimation results of gear image
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Table 2. Noise estimation results of gear image
Iteration 2nd iteration 3rd iteration 4th iteration
Estimationresult $\begin{matrix} {\hat{σ}}_{2} \end{matrix}$ =7.285 $\begin{matrix} {\hat{σ}}_{3} \end{matrix}$ =6.191 $\begin{matrix} {\hat{σ}}_{4} \end{matrix}$ =2.716

Table 3. Comparison of denoising results of gear image

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Table 3. Comparison of denoising results of gear image

Image	R_PSNR	M_MSSIM
Noise image	16.387	0.766
Image obtained when imagestandard deviation is usedas denoising parameter	17.079	0.891
Image after 2nd iteration	17.095	0.901
Image after 3rd iteration	17.095	0.904
Image after 4th iteration	17.095	0.904

Table 4. Objective quality parameters of BNLM and NLM denoising of gear image
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Table 4. Objective quality parameters of BNLM and NLM denoising of gear image
Processing method R_PSNR M_MSSIM
BNLM 17.074 0.891
NLM 16.753 0.879

Table 5. Noise estimation results of Fuwa image
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Table 5. Noise estimation results of Fuwa image
Iteration 1st iteration 2nd iteration 3rd iteration 4th iteration 5th iteration
Estimation result $\begin{matrix} {\hat{σ}}_{1} \end{matrix}$ =44.191 $\begin{matrix} {\hat{σ}}_{2} \end{matrix}$ =19.666 $\begin{matrix} {\hat{σ}}_{3} \end{matrix}$ =27.435 $\begin{matrix} {\hat{σ}}_{4} \end{matrix}$ =28.310 $\begin{matrix} {\hat{σ}}_{5} \end{matrix}$ =31.758

Table 6. Comparison of denoising results of Fuwa image

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Table 6. Comparison of denoising results of Fuwa image

Image	S_SNT
Noise image	6.255
Image obtained when imagestandard deviation is usedas denoising parameter	6.313
Image obtained when noisestandard deviation is usedas denoising parameter	6.176
Image after 2nd iteration	6.234
Image after 3rd iteration	6.307
Image after 4th iteration	6.382
Image after 5th iteration	6.459

Table 7. Objective quality parameters of BNLM and NLM denoising of Fuwa image
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Table 7. Objective quality parameters of BNLM and NLM denoising of Fuwa image
Processing method BNLM NLM
S_SNT 6.296 5.661