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Fig 1.

The 14 test images for image denoising.

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Fig 1 Expand

Fig 2.

Illustration of the over-shrinkage problem with the value of power p.

(A) Original image. (B) Noisy image with σn = 50. (C) Singular values of .

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Fig 2 Expand

Fig 3.

The influence of changing p upon denoised results under different noise levels.

(A) σn = 20 (B) σn = 30 (C) σn = 50 (D) σn = 60 (E) σn = 75 (F) σn = 100.

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Fig 3 Expand

Table 1.

Denoising results of different algorithms for given noise level σn = 20.

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Table 1 Expand

Table 2.

Denoising results of different algorithms for given noise level σn = 30.

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Table 2 Expand

Table 3.

Denoising results of different algorithms for given noise level σn = 50.

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Table 3 Expand

Table 4.

Denoising results of different algorithms for given noise level σn = 60.

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Table 5.

Denoising results of different algorithms for given noise level σn = 75.

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Table 6.

Average denoising result of different algorithms for given noise level σn = 100.

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Table 6 Expand

Table 7.

Comparison of average PNSR with different methods.

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Table 7 Expand

Fig 4.

Denoising results on image Cameraman by different methods (noise level σn = 20).

(A) Ground Truth (B) Noisy Image (C) BM3D, PSNR = 30.48 (D) EPLL, PSNR = 30.34 (E) SAIST, PSNR = 30.45 (F) WNNM, PSNR = 30.64 (G) WSNM, PSNR = 30.64 (H) SAFPI, PSNR = 30.68.

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Fig 4 Expand

Fig 5.

Denoising results on image Monarch by different methods (noise level σn = 30).

(A) Ground Truth (B) Noisy Image (C) BM3D, PSNR = 28.36 (D) EPLL, PSNR = 28.35 (E) SAIST, PSNR = 28.03 (F) WNNM, PSNR = 28.94 (G) WSNM, PSNR = 29.02 (H) SAFPI, PSNR = 29.09.

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Fig 5 Expand

Fig 6.

Denoising results on image House by different methods (noise level σn = 50).

(A) Ground Truth (B) Noisy Image (C) BM3D, PSNR = 29.69 (D) EPLL, PSNR = 28.76 (E) SAIST, PSNR = 29.99 (F) WNNM, PSNR = 30.28 (G) WSNM, PSNR = 30.21 (H) SAFPI, PSNR = 30.51.

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Fig 6 Expand

Fig 7.

Denoising results on image Barbara by different methods (noise level σn = 100).

(A) Ground Truth (B) Noisy Image (C) BM3D, PSNR = 23.62 (D) EPLL,PSNR = 22.14 (E) SAIST, PSNR = 23.98 (F) WNNM, PSNR = 24.39 (G) WSNM, PSNR = 24.43 (H) SAFPI, PSNR = 24.5.

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Fig 7 Expand

Table 8.

Average denoising results of different algorithms for Speckle-Gaussian noise.

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Table 8 Expand

Table 9.

Average denoising results of different algorithms for Poisson-Gaussian noise.

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Table 9 Expand