Adaptive denoising method of steel plate surface image based on BM3D

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

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

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

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

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

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

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

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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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Paper Information

Category: Information Sciences

Received: Feb. 21, 2022

Accepted: --

Published Online: Oct. 27, 2022

The Author Email: LI Yibo (yibo.li@csu.edu.cn)

DOI:10.37188/OPE.20223020.2510

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. 为10时新算法参数设置

Table 1. 为10时新算法参数设置

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

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

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

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

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

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

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