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Table 2 Comparisons of peak signal-to-noise ratio(PSNR) values, structural similarity (SSIM) values, and feature similarity (FSIM) values for image with different noise level with different super-resolution approaches

From: Group-based single image super-resolution with online dictionary learning

Noise level

 

Bi-cubic

Yang et al. [20]

Glasner et al. [15]

ASDS [13]

Proposed

σ ν =0

PSNR

28.93

31.21

30.19

3 4.0 5

32.92

 

SSIM

0.911

0.926

0.939

0.945

0.9 4 9

 

FSIM

0.917

0.948

0.938

0.9 5 9

0.957

σ ν =1

PSNR

28.92

31.13

30.17

31.34

3 2.9 2

 

SSIM

0.910

0.918

0.935

0.908

0.9 4 7

 

FSIM

0.917

0.945

0.937

0.934

0.9 5 6

σ ν =3

PSNR

28.83

30.49

29.94

31.24

3 2.6 7

 

SSIM

0.897

0.863

0.908

0.907

0.9 3 4

 

FSIM

0.913

0.918

0.925

0.936

0.9 5 2

σ ν =5

PSNR

28.66

29.41

29.46

30.96

3 2.1 8

 

SSIM

0.875

0.779

0.860

0.896

0.9 1 1

 

FSIM

0.904

0.872

0.901

0.934

0.9 4 1

  1. The data in italics indicates the best PSNR/SSIM/FSIM result