Adaptive denoising method of steel plate surface image based on BM3D

Yi YANG; Yibo LI; Zhuxi MA; Fengyu CHEN; Qianbin HUANG

doi:10.37188/OPE.20223020.2510

Journals >Optics and Precision Engineering >Volume 30 >Issue 20 >Page 2510 > Article

Optics and Precision Engineering
Vol. 30, Issue 20, 2510 (2022)

Adaptive denoising method of steel plate surface image based on BM3D

Yi YANG, Yibo LI^*, Zhuxi MA, Fengyu CHEN, and Qianbin HUANG

Author Affiliations

Light Alloy Research Institute of Central South University， Changsha410083， China

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DOI: 10.37188/OPE.20223020.2510 Cite this Article

Yi YANG, Yibo LI, Zhuxi MA, Fengyu CHEN, Qianbin HUANG. Adaptive denoising method of steel plate surface image based on BM3D[J]. Optics and Precision Engineering, 2022, 30(20): 2510 Copy Citation Text

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Fig. 1. Flow chart of BM3D algorithm

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Fig. 2. Fitting function graph of basic estimate threshold

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Fig. 3. Fitting function graph of final estimate threshold

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Fig. 4. Flow chart of TFBM3D algorithm denoising

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Fig. 5. Three testing images

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Fig. 6. PSNR and SSIM curves of each algorithm on the scratch image

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Fig. 7. PSNR and SSIM curves of each algorithm on the inclusion image

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Fig. 8. PSNR and SSIM curves of each algorithm on the pitted-surface image

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Fig. 9. Denosing performance of different algorithms on the scratch image while σ=10

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Fig. 10. Denosing performance of different algorithms on the inclusion image while σ=30

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Fig. 11. Denosing performance of different algorithms on the pitted-surface image while σ=25

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Fig. 12. Residual images of different denosing algorithms on the pitted-surface image while σ=25

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Fig. 13. Image defect segmentation effect of different denosing algorithms on the scratch image while σ=10

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参数	基础估计数值	最终估计数值
预滤波收缩系数 $λ_{2 D}$	0.2	\
硬阈值收缩系数 $λ_{3 D}$	3.2	\
图像块尺寸 $N$	16	16
参考块移动步长	3	3
搜索块步长	1	1
距离阈值 $τ {}_{1}(τ_{2})$	365.57	211.85
最大相似块数	16	16
搜索窗尺寸 $W_{1} (W_{2})$	39	39
凯撒值 $β$	2.0	2.0

Table 1. 为10时新算法参数设置

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Algorithm	PSNR/dB					SSIM
Algorithm	10	15	20	25	30	10	15	20	25	30
GF	35.48	32.57	30.24	28.34	26.91	0.82	0.71	0.60	0.50	0.41
TSNLM	36.52	34.11	31.67	29.86	28.43	0.89	0.83	0.74	0.64	0.56
TSF	35.70	34.24	32.44	30.86	29.39	0.88	0.83	0.75	0.67	0.59
CTWT	36.61	36.01	34.91	33.75	28.34	0.89	0.88	0.87	0.84	0.57
IPNLM	37.71	36.54	35.16	33.54	32.10	0.90	0.89	0.85	0.82	0.71
BM3D	37.83	36.85	35.97	34.88	34.68	0.90	0.89	0.88	0.86	0.84
ABM3D	37.30	35.49	33.60	32.24	30.84	0.90	0.89	0.88	0.86	0.84
TFBM3D	38.04	37.53	36.83	36.43	36.04	0.91	0.90	0.90	0.89	0.89

Table 2. PSNR and SSIM indexes of different denoising algorithms on the scratch image

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Algorithm	PSNR/dB					SSIM
Algorithm	10	15	20	25	30	10	15	20	25	30
GF	35.83	32.73	30.25	28.45	27.00	0.84	0.73	0.61	0.51	0.43
TSNLM	36.77	34.32	31.84	30.17	28.59	0.90	0.84	0.73	0.65	0.58
TSF	35.84	34.23	32.42	30.78	29.38	0.87	0.81	0.74	0.66	0.59
CTWT	36.31	35.89	34.91	32.56	28.42	0.89	0.88	0.87	0.78	0.55
IPNLM	38.09	36.83	35.40	34.26	33.14	0.91	0.90	0.86	0.82	0.78
BM3D	38.20	37.03	35.91	34.30	33.13	0.91	0.90	0.88	0.87	0.84
ABM3D	37.56	35.46	33.72	32.27	31.14	0.90	0.85	0.80	0.74	0.70
TFBM3D	38.51	37.69	36.83	36.33	35.81	0.92	0.91	0.90	0.90	0.89

Table 3. PSNR and SSIM indexes of different denoising algorithms on the inclusion image

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Algorithm	PSNR/dB					SSIM
Algorithm	10	15	20	25	30	10	15	20	25	30
GF	35.89	32.58	30.32	28.42	27.04	0.86	0.75	0.65	0.59	0.47
TSNLM	36.75	33.94	31.95	30.04	28.61	0.91	0.85	0.78	0.69	0.62
TSF	34.59	33.04	31.35	30.02	28.55	0.84	0.80	0.72	0.65	0.60
CTWT	36.03	34.15	32.67	31.48	30.14	0.88	0.81	0.79	0.75	0.71
IPNLM	37.19	35.57	34.45	33.26	32.42	0.91	0.88	0.85	0.81	0.78
BM3D	37.33	35.74	34.75	33.71	33.00	0.92	0.90	0.88	0.85	0.83
ABM3D	37.30	35.03	33.46	31.84	30.65	0.91	0.87	0.82	0.76	0.70
TFBM3D	37.65	36.15	35.22	34.25	33.58	0.92	0.90	0.88	0.87	0.85

Table 4. PSNR and SSIM indexes of different denoising algorithms on the pitted-surface image

Yi YANG, Yibo LI, Zhuxi MA, Fengyu CHEN, Qianbin HUANG. Adaptive denoising method of steel plate surface image based on BM3D[J]. Optics and Precision Engineering, 2022, 30(20): 2510

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