Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1950-1956.DOI: 10.11772/j.issn.1001-9081.2021040620
• Multimedia computing and computer simulation • Previous Articles Next Articles
Lulu LEI1,2, Yingyue ZHOU1,2(), Chi LI1,2, Xinyu WANG1,2, Jiaqi ZHAO1,2
Received:
2021-04-20
Revised:
2021-07-01
Accepted:
2021-07-20
Online:
2022-06-22
Published:
2022-06-10
Contact:
Yingyue ZHOU
About author:
LEI Lulu,born in 1997,M. S. candidate. Her research interests include image restoration.Supported by:
雷露露1,2, 周颖玥1,2(), 李驰1,2, 王欣宇1,2, 赵家琦1,2
通讯作者:
周颖玥
作者简介:
雷露露(1997—),女,四川广安人,硕士研究生,主要研究方向:图像恢复基金资助:
CLC Number:
Lulu LEI, Yingyue ZHOU, Chi LI, Xinyu WANG, Jiaqi ZHAO. Speckle removal algorithm for ultrasonic image based on multi-scale fast non-local means filtering[J]. Journal of Computer Applications, 2022, 42(6): 1950-1956.
雷露露, 周颖玥, 李驰, 王欣宇, 赵家琦. 基于多尺度快速非局部平均滤波的超声图像去斑算法[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1950-1956.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040620
噪声 强度 | 局部图像1 | 局部图像2 | ||||
---|---|---|---|---|---|---|
5 | 19.98 | 37.07 | 61.17 | 12.68 | 29.55 | 76.07 |
15 | 111.80 | 208.40 | 397.20 | 96.87 | 245.50 | 447.30 |
25 | 337.00 | 829.40 | 1 430.00 | 283.30 | 520.80 | 953.90 |
Tab. 1 Similarity distance d values for different image block sizes
噪声 强度 | 局部图像1 | 局部图像2 | ||||
---|---|---|---|---|---|---|
5 | 19.98 | 37.07 | 61.17 | 12.68 | 29.55 | 76.07 |
15 | 111.80 | 208.40 | 397.20 | 96.87 | 245.50 | 447.30 |
25 | 337.00 | 829.40 | 1 430.00 | 283.30 | 520.80 | 953.90 |
匹配区域 | 搜索区域 | 算法的运行时间/s | NLMF与F-NLMF运行时间比 | IBNLMF与F-NLMF运行时间比 | ||
---|---|---|---|---|---|---|
NLMF | IBNLMF | F-NLMF | ||||
5×5 | 13×13 | 45.19 | 3.12 | 1.79 | 25.25 | 1.74 |
5×5 | 73×73 | 1 202.39 | 37.61 | 7.75 | 155.15 | 4.85 |
7×7 | 13×13 | 48.81 | 3.34 | 1.85 | 26.38 | 1.80 |
7×7 | 73×73 | 1 841.00 | 38.70 | 8.53 | 214.82 | 4.52 |
9×9 | 13×13 | 65.00 | 3.60 | 1.98 | 32.82 | 1.82 |
9×9 | 73×73 | 2 040.80 | 39.96 | 9.08 | 226.67 | 4.40 |
Tab. 2 Comparison of despeckling speed of different algorithms to “head” phantom image with speckle noise
匹配区域 | 搜索区域 | 算法的运行时间/s | NLMF与F-NLMF运行时间比 | IBNLMF与F-NLMF运行时间比 | ||
---|---|---|---|---|---|---|
NLMF | IBNLMF | F-NLMF | ||||
5×5 | 13×13 | 45.19 | 3.12 | 1.79 | 25.25 | 1.74 |
5×5 | 73×73 | 1 202.39 | 37.61 | 7.75 | 155.15 | 4.85 |
7×7 | 13×13 | 48.81 | 3.34 | 1.85 | 26.38 | 1.80 |
7×7 | 73×73 | 1 841.00 | 38.70 | 8.53 | 214.82 | 4.52 |
9×9 | 13×13 | 65.00 | 3.60 | 1.98 | 32.82 | 1.82 |
9×9 | 73×73 | 2 040.80 | 39.96 | 9.08 | 226.67 | 4.40 |
算法 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | |
斑点图像 | 33.55 | 0.813 3 | 27.45 | 0.721 3 | 23.72 | 0.660 0 | 21.50 | 0.625 5 | 19.94 | 0.604 2 |
SARD | 31.53 | 0.974 3 | 29.93 | 0.941 7 | 27.07 | 0.814 2 | 24.69 | 0.739 1 | 22.85 | 0.689 4 |
SRBF | 25.45 | 0.863 1 | 24.72 | 0.838 7 | 23.31 | 0.812 3 | 23.01 | 0.799 0 | 22.31 | 0.790 9 |
TBNLMF | 38.88 | 0.957 4 | 32.66 | 0.894 6 | 28.66 | 0.855 5 | 25.40 | 0.827 1 | 22.07 | 0.796 7 |
OBNLMF | 38.74 | 0.945 8 | 32.42 | 0.872 7 | 28.33 | 0.801 9 | 25.41 | 0.737 8 | 23.14 | 0.701 2 |
WRNLMF | 39.89 | 0.967 3 | 33.66 | 0.918 6 | 30.18 | 0.885 5 | 28.13 | 0.851 7 | 25.82 | 0.830 5 |
IBNLMF | 40.39 | 0.974 9 | 34.16 | 0.942 4 | 30.42 | 0.920 6 | 28.14 | 0.859 4 | 25.64 | 0.851 8 |
MF-NLMF | 41.20 | 0.985 9 | 35.70 | 0.968 2 | 31.86 | 0.944 0 | 29.27 | 0.940 3 | 26.55 | 0.930 8 |
Tab.3 Despeckling results obtained by different algorithms to “head” phantom image with speckle noise
算法 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | |
斑点图像 | 33.55 | 0.813 3 | 27.45 | 0.721 3 | 23.72 | 0.660 0 | 21.50 | 0.625 5 | 19.94 | 0.604 2 |
SARD | 31.53 | 0.974 3 | 29.93 | 0.941 7 | 27.07 | 0.814 2 | 24.69 | 0.739 1 | 22.85 | 0.689 4 |
SRBF | 25.45 | 0.863 1 | 24.72 | 0.838 7 | 23.31 | 0.812 3 | 23.01 | 0.799 0 | 22.31 | 0.790 9 |
TBNLMF | 38.88 | 0.957 4 | 32.66 | 0.894 6 | 28.66 | 0.855 5 | 25.40 | 0.827 1 | 22.07 | 0.796 7 |
OBNLMF | 38.74 | 0.945 8 | 32.42 | 0.872 7 | 28.33 | 0.801 9 | 25.41 | 0.737 8 | 23.14 | 0.701 2 |
WRNLMF | 39.89 | 0.967 3 | 33.66 | 0.918 6 | 30.18 | 0.885 5 | 28.13 | 0.851 7 | 25.82 | 0.830 5 |
IBNLMF | 40.39 | 0.974 9 | 34.16 | 0.942 4 | 30.42 | 0.920 6 | 28.14 | 0.859 4 | 25.64 | 0.851 8 |
MF-NLMF | 41.20 | 0.985 9 | 35.70 | 0.968 2 | 31.86 | 0.944 0 | 29.27 | 0.940 3 | 26.55 | 0.930 8 |
算法 | 第一个区域 | 第二个区域 | 第三个区域 | |||
---|---|---|---|---|---|---|
CNR | SNR | CNR | SNR | CNR | SNR | |
噪声图像 | 0.046 8 | 0.112 4 | 0.019 0 | 0.059 3 | 0.012 0 | 0.156 3 |
SARD | 0.112 8 | 0.275 6 | 0.026 7 | 0.082 4 | 0.047 6 | 0.590 1 |
SRBF | 0.093 3 | 0.238 1 | 0.023 9 | 0.074 1 | 0.022 6 | 0.313 4 |
TBNLMF | 0.186 6 | 0.512 8 | 0.028 7 | 0.084 8 | 0.078 2 | 0.942 0 |
OBNLMF | 0.079 4 | 0.200 4 | 0.026 0 | 0.080 8 | 0.057 4 | 0.658 5 |
WRNLMF | 0.384 1 | 1.080 8 | 0.033 5 | 0.097 8 | 0.164 2 | 2.275 3 |
IBNLM | 0.396 2 | 1.344 6 | 0.038 6 | 0.117 1 | 0.222 6 | 3.775 9 |
MF-NLMF | 0.420 2 | 1.472 1 | 0.039 1 | 0.118 7 | 0.240 7 | 3.784 0 |
Tab. 4 Despeckling quantitative results of different despeckling algorithms for Field Ⅱ simulated “cyst” ultrasound image unit: dB
算法 | 第一个区域 | 第二个区域 | 第三个区域 | |||
---|---|---|---|---|---|---|
CNR | SNR | CNR | SNR | CNR | SNR | |
噪声图像 | 0.046 8 | 0.112 4 | 0.019 0 | 0.059 3 | 0.012 0 | 0.156 3 |
SARD | 0.112 8 | 0.275 6 | 0.026 7 | 0.082 4 | 0.047 6 | 0.590 1 |
SRBF | 0.093 3 | 0.238 1 | 0.023 9 | 0.074 1 | 0.022 6 | 0.313 4 |
TBNLMF | 0.186 6 | 0.512 8 | 0.028 7 | 0.084 8 | 0.078 2 | 0.942 0 |
OBNLMF | 0.079 4 | 0.200 4 | 0.026 0 | 0.080 8 | 0.057 4 | 0.658 5 |
WRNLMF | 0.384 1 | 1.080 8 | 0.033 5 | 0.097 8 | 0.164 2 | 2.275 3 |
IBNLM | 0.396 2 | 1.344 6 | 0.038 6 | 0.117 1 | 0.222 6 | 3.775 9 |
MF-NLMF | 0.420 2 | 1.472 1 | 0.039 1 | 0.118 7 | 0.240 7 | 3.784 0 |
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