Image Noise Reduction in Computed Tomography with Non-Local Means Algorithm Based on Adaptive Filtering Coefficients

Yufang Cai; Taoyan Chen; Jue Wang; Gongjie Yao

doi:10.3788/AOS202040.0710001

Acta Optica Sinica, Volume. 40, Issue 7, 0710001(2020)

Image Noise Reduction in Computed Tomography with Non-Local Means Algorithm Based on Adaptive Filtering Coefficients

Yufang Cai^1,2、*, Taoyan Chen^1,2, Jue Wang^1,2, and Gongjie Yao^1,2

¹Engineering Research Center of Industrial CT Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China

²College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China

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Figures & Tables(14)

Fig. 1. Relationship diagram of similar window and search window of NLM algorithm

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Fig. 2. Structural tensor decomposition. (a) Original image; (b) schematic of structure tensor

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Fig. 3. Flowchart of ST-NLM algorithm

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Fig. 4. Gaussian noise simulated image and local magnification of filtering results at different noise levels. (a) Simulated image; (b) σ=1; (c) σ=4; (d) σ=5; (e) σ=12

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Fig. 5. CT image of spatial resolution testing card. (a) Original image; (b) NLM; (c) method in Ref. [12]; (d) ST-NLM

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Fig. 6. Gray curves obtained by different filtering methods

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Fig. 7. Typical CT slices of insect 1. (a) Original image; (b) locally enlarged image; (c) NLM; (d) method in Ref. [12]; (e) ST-NLM

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Fig. 8. CT image of the 520^th slice of insect 1. (a) Original image; (b) locally enlarged image; (c) NLM; (d) method in Ref. [12]; (e) ST-NLM

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Fig. 9. CT image of the 434^th slice of insect 2. (a) Original image; (b) locally enlarged image; (c) NLM; (d) method in Ref. [12]; (e) ST-NLM

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Table 1. Quantitative evaluation of SSIM and PSNR by different methods

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Table 1. Quantitative evaluation of SSIM and PSNR by different methods

Evaluation parameter	σ	Noisy image	NLM	Method in Ref. [12]	ST-NLM
	1	0.2771	0.9444	0.9553	0.9556
S_SIM	4	0.1987	0.8472	0.9025	0.9101
	5	0.1629	0.7997	0.8241	0.8663
	12	0.1150	0.6276	0.6722	0.7309
	1	23.54	30.86	32.04	36.39
R_PSN /dB	4	21.84	30.11	31.17	33.35
	5	18.64	28.03	29.06	29.67
	12	15.31	24.19	25.01	25.08

Table 2. CT system parameters for experiment

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Table 2. CT system parameters for experiment

Object	CT system	X-ray energy /keV	Detector size /mm	Exposure time /ms	Views of projection
Spatial resolution testing card	Linear array	4000	1.500	20	2048
Insect 1	Planer array	150	0.200	500	500
Insect 2	Planer array	60	0.075	1000	500

Table 3. Experimental parameters of different images

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Table 3. Experimental parameters of different images

Image	Image size	Similar window size	Search window size	σ
Fig. 5	336×283	3×3	7×7	0.34
	768×1024	3×3	7×7	0.11
Fig. 7	768×1024	3×3	7×7	0.14
	768×1024	3×3	7×7	0.12
	768×1024	3×3	7×7	0.10
Fig. 8	768×1024	3×3	7×7	0.15
Fig. 9	1200×1200	3×3	7×7	0.25

Table 4. Index of image sharpness

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Table 4. Index of image sharpness

Image	Evaluation parameter	Slice No.	NLM	Method in Ref. [12]	ST-NLM
		340^th	0.0868	0.0941	0.0995
Fig. 7	Global Tenengrad	592^th	0.1127	0.1209	0.1416
		636^th	0.1063	0.1135	0.1355
		710^th	0.0906	0.0978	0.0966
Fig. 8	Global Tenengrad	520^th	0.0882	0.0902	0.1021
Fig. 9	Global Tenengrad	434^th	0.0911	0.0944	0.1113

Table 5. Comparison of operating time among various filtering algorithmss
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Table 5. Comparison of operating time among various filtering algorithmss
Image Image size NLM NLM integrating image acceleration Method in Ref. [12] ST-NLM
Fig. 5 336×283 3.18 0.53 3.21 2.05
Fig. 8 768×1024 20.10 1.78 21.10 8.06
Fig. 9 1200×1200 37.90 3.86 38.30 15.30

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Yufang Cai, Taoyan Chen, Jue Wang, Gongjie Yao. Image Noise Reduction in Computed Tomography with Non-Local Means Algorithm Based on Adaptive Filtering Coefficients[J]. Acta Optica Sinica, 2020, 40(7): 0710001

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