Gamma-Ray Noise Removal Based on Video Time Series Correlation

Lei Deng; Guihua Liu; Hao Deng; Ling Cao

doi:10.3788/LOP202259.1810005

Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1810005(2022)

Gamma-Ray Noise Removal Based on Video Time Series Correlation

Lei Deng^1,2, Guihua Liu^1,2、*, Hao Deng^1,2, and Ling Cao^1,2

Author Affiliations

¹School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan , China

²Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, Sichuan , China

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Figures & Tables(13)

Fig. 1. Noise example in γ nuclear radiation scene. (a) Noise map; (b) bright patch noise; (c) dark patch noise

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Fig. 2. Variation of image quality in γ radiation scene with increase of irradiation time. (a) Variation of PSNR; (b) variation of SSIM

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Fig. 3. Detection results of bright patch noise in different γ radiation scene images. (a) Noise map in scene 1; (b) detection results of bright patch noise in [Fig.3(a)]; (c) binarization of [Fig.3(b)]; (d) noise map in scene 2; (e) detection results of bright patch noise in [Fig.3(d)]; (f) binarization of [Fig.3(e)]

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Fig. 4. Detection results of dark patch noise in different γ radiation scene images. (a) Scene 1 pixel flip; (b) detection results of dark patch noise in [Fig.4(a)]; (c) binarization of [Fig.4(b)]; (d) scene 2 pixel flip; (e) detection results of dark patch noise in [Fig.4(d)]; (f) binarization of [Fig.4(e)]

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Fig. 5. Time series correlation method for transient noise removal

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Fig. 6. Denoising effect of different number of near frame images with different irradiation time. (a) PNSR value after denoising; (b) SSIM value after denoising

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Fig. 7. Results of denoising and enhancement in different γ radiation scene images. (a) Noise map in scene 1; (b) denoising results of [Fig.7(a)]; (c) enhancement results of [Fig.7(b)]; (d) noise map in scene 2; (e) denoising result of [Fig.7(d)]; (f) enhancement result of [Fig.7(e)]

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Fig. 8. Quality of noiseless image changes with average number of frames increase. (a) Variation of PSNR value; (b) variation of SSIM value

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Fig. 9. Comparison of denoising results in 200 Gy/h γ radiation scene images. (a) Noise map; (b) median; (c) wavelet; (d) anisotropy;(e) PDE; (f) BM3D; (g) NLM; (h) guide; (i) TV; (j) proposed algorithm

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Fig. 10. Comparison of denoising results in 20 Gy/h γ radiation scene images. (a) Noise map; (b) median; (c) wavelet; (d) anisotropy;(e) PDE; (f) BM3D; (g) NLM; (h) guide; (i) TV; (j) proposed algorithm

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Table 1. PSNR of denoising results in γ radiation scene images with $r 、 n 、 L_{t}$ change
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Table 1. PSNR of denoising results in γ radiation scene images with $r 、 n 、 L_{t}$ change
L_t r=0.25 r=0.5 r=0.75
n=1 n=2 n=3 n=1 n=2 n=3 n=1 n=2 n=3
5 32.56 32.78 32.81 31.5 31.96 32.23 24.48 29.5 30.23
10 31.59 31.68 32.2 26.52 31.2 31.71 23.29 26.22 29.93
15 30.43 30.73 31.48 24.55 29.43 30.03 22.48 23.45 28.48

Table 2. Quantitative comparison of denoising results

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Table 2. Quantitative comparison of denoising results

Dose /（Gy·h^-1）	Index	Noise	Median	Wavelet	Anisotropy	PDE	BM3D	NLM	Guide	TV	Proposed algorithm
200	PSNR /dB	16.72	20.89	19.68	16.80	21.78	16.95	16.93	21.95	18.86	32.66
200	SSIM	0.28	0.47	0.36	0.32	0.44	0.31	0.31	0.47	0.36	0.88
20	PSNR /dB	23.48	24.22	23.85	23.22	26.25	23.52	23.65	26.20	25.50	33.43
20	SSIM	0.65	0.67	0.65	0.71	0.79	0.65	0.68	0.79	0.76	0.94

Table 3. Quantitative comparison of denoising results in different size images

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Table 3. Quantitative comparison of denoising results in different size images

Image size	Index	Noise	Median	Wavelet	Anisotropy	PDE	BM3D	NLM	Guide	TV	Proposed algorithm
500×500	PSNR /dB	16.90	20.90	20.05	16.80	19.16	16.95	16.90	21.96	24.16	32.73
500×500	SSIM	0.30	0.47	0.41	0.32	0.41	0.31	0.31	0.50	0.67	0.87
600×600	PSNR /dB	16.87	20.73	19.95	16.83	19.15	16.92	16.88	21.86	23.54	32.68
600×600	SSIM	0.31	0.47	0.40	0.32	0.41	0.31	0.31	0.50	0.64	0.88
700×700	PSNR /dB	16.83	20.64	19.74	16.84	19.02	16.88	16.84	21.67	23.00	32.68
700×700	SSIM	0.31	0.47	0.39	0.33	0.41	0.31	0.31	0.50	0.64	0.88
800×800	PSNR /dB	16.91	20.77	19.81	16.94	19.13	16.96	16.91	21.77	22.75	1
800×800	SSIM	0.31	0.48	0.40	0.34	0.42	0.32	0.32	0.50	0.64	0.88
900×900	PSNR /dB	16.98	20.89	19.91	17.06	19.21	17.03	16.99	21.85	22.71	32.62
900×900	SSIM	0.32	0.48	0.40	0.34	0.42	0.323	0.33	0.50	0.65	0.87
1000×1000	PSNR /dB	16.94	20.87	19.90	17.06	19.17	16.99	16.95	21.85	22.86	32.65
1000×1000	SSIM	0.31	0.47	0.39	0.33	0.41	0.31	0.32	0.50	0.66	0.87

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Lei Deng, Guihua Liu, Hao Deng, Ling Cao. Gamma-Ray Noise Removal Based on Video Time Series Correlation[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810005

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Paper Information

Category: Image Processing

Received: Jun. 11, 2021

Accepted: Jul. 20, 2021

Published Online: Aug. 22, 2022

The Author Email: Liu Guihua (liughua_Swit@163.com)

DOI:10.3788/LOP202259.1810005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. PSNR of denoising results in γ radiation scene images with r、n、Lt change

Table 1. PSNR of denoising results in γ radiation scene images with r、n、Lt change

Table 2. Quantitative comparison of denoising results

Table 2. Quantitative comparison of denoising results

Table 3. Quantitative comparison of denoising results in different size images

Table 3. Quantitative comparison of denoising results in different size images

Table 1. PSNR of denoising results in γ radiation scene images with $r 、 n 、 L_{t}$ change

Table 1. PSNR of denoising results in γ radiation scene images with $r 、 n 、 L_{t}$ change