Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 83-90.doi: 10.6040/j.issn.1672-3961.0.2019.262

• Machine Learning & Data Mining • Previous Articles     Next Articles

Image denoising based on 3D shearlet transform and BM4D

Shengnan ZHANG(),Lei WANG*(),Chunhong CHANG,Benli HAO   

  1. College of Computer Science and Technology, Shandong University of Technology, Zibo 255000, Shandong, China
  • Received:2019-05-28 Online:2020-04-20 Published:2020-04-16
  • Contact: Lei WANG E-mail:644612607@qq.com;wanglei0511@sdut.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61502282);山东省自然科学基金资助项目(ZR2015FQ005);山东省高等学校科技计划资助项目(J18KA362);山东省智慧矿山信息技术重点实验室开放基金资助项目

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Abstract:

Aimed at the disadvantage that the traditional block matching denoising method could only deal with two-dimensional images, an image denoising method based on 3D shearlet transform and BM4D(block-matching and 4D filtering) was proposed. This method used 3D shearlet transform to obtain transform domain coefficients, and realized joint filtering in transform domain through hard threshold and Wiener filtering stage. The 3D shearlet transformation was localized through two filtering stages: multi-scale decomposition and directional decomposition. The hard threshold and Wiener filtering were performed, which include grouping, collaborative filtering and aggregation. The 4D transformation of the cubes was based on the local correlationandon-local correlation cubes. The estimated values of each grouped cube were obtained by inverse transformation of 3D shearlet transform, and self-adaptive aggregation was performed at their original positions. PSNR(peak signal to noise ratio) and SSIM(structural similarity) were used as evaluation criteria. The results showed that this method could effectively remove image noise in high noise environment, and effectively improved the visual effect of the image with high accuracy.

Key words: 3D shearlet transform, combined filtering, collaborative filtering, non-local correlation, self-adaptive aggregation

CLC Number: 

  • TP391.4

Fig.1

Comparison of classical denoising images and PSNR"

Fig.2

Partial effect comparisonof Gauss noise"

Fig.3

Partial effect comparison of Rician noise"

Table 1

Comparison of the denoising performance of Gaussian noise"

去噪方法 评价指标 σ/%
1 3 5 7 9 11 13 15 17 19
噪声数据 PSNR 40.00 30.46 26.02 23.10 20.91 19.17 17.72 16.48 15.39 14.42
SSIM 0.97 0.81 0.66 0.53 0.43 0.36 0.30 0.25 0.22 0.19
OB-NLM3D PSNR 42.47 37.57 34.73 32.82 31.42 30.32 29.40 28.61 27.91 27.28
SSIM 0.99 0.97 0.95 0.92 0.90 0.87 0.84 0.82 0.79 0.77
OB-NLM3D-WM PSNR 42.52 37.75 35.01 33.13 31.73 30.61 29.68 28.88 28.18 27.55
SSIM 0.99 0.97 0.95 0.93 0.90 0.88 0.85 0.83 0.80 0.78
ODCT3D PSNR 43.78 37.53 34.89 33.18 31.91 30.90 30.07 29.35 28.73 28.18
SSIM 0.99 0.97 0.95 0.93 0.91 0.89 0.88 0.86 0.85 0.83
PRI-NLM3D PSNR 44.04 38.26 35.51 33.67 32.37 31.29 30.40 29.65 28.99 28.40
SSIM 0.99 0.98 0.96 0.94 0.92 0.90 0.89 0.87 0.85 0.84
BM4D PSNR 44.09 38.39 35.95 34.38 33.21 32.28 31.50 30.82 30.23 29.70
SSIM 0.99 0.98 0.96 0.95 0.93 0.92 0.91 0.90 0.88 0.87
本研究 PSNR 45.00 39.19 36.62 34.94 33.70 32.70 31.88 31.19 30.58 30.05
SSIM 0.99 0.97 0.96 0.94 0.93 0.92 0.90 0.89 0.88 0.87

Table 2

Comparison of the denoising performance of Rician noise."

去噪方法 评价指标 σ/%
1 3 5 7 9 11 13 15 17 19
噪声数据 PSNR 40.00 30.49 26.09 23.20 21.04 19.32 17.88 16.65 15.57 14.60
SSIM 0.97 0.81 0.66 0.53 0.43 0.36 0.30 0.25 0.21 0.18
OB-NLM3D PSNR 42.41 37.45 34.54 32.51 30.97 29.71 28.62 27.64 26.74 25.91
SSIM 0.99 0.97 0.94 0.91 0.88 0.85 0.81 0.78 0.74 0.70
OB-NLM3D-WM PSNR 42.44 37.54 34.66 32.61 31.01 29.69 28.53 27.50 26.57 25.71
SSIM 0.99 0.97 0.95 0.92 0.88 0.85 0.81 0.77 0.74 0.70
ODCT3D PSNR 42.96 37.38 34.70 32.90 31.53 30.41 29.48 28.67 27.95 27.30
SSIM 0.99 0.97 0.95 0.93 0.90 0.88 0.86 0.84 0.82 0.80
PRI-NLM3D PSNR 43.97 38.19 35.34 33.37 31.94 30.74 29.75 28.88 28.10 27.39
SSIM 0.99 0.98 0.96 0.94 0.91 0.89 0.87 0.85 0.82 0.80
BM4D PSNR 44.08 38.34 35.83 34.17 32.89 31.82 30.90 30.06 29.29 28.57
SSIM 0.99 0.98 0.96 0.94 0.93 0.91 0.89 0.88 0.86 0.84
本研究 PSNR 45.00 39.18 36.60 34.88 33.59 32.54 31.66 30.91 30.23 29.59
SSIM 0.99 0.97 0.96 0.94 0.93 0.91 0.90 0.89 0.87 0.85
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