Skip to main content

Table 3 PSNR (top) and SSIM (bottom), comparison of the bicubic (Bic.) technique, algorithm of Yang et al.’s [11], algorithm of Xu et al.’s [12], and the proposed algorithm

From: Single image super-resolution by directionally structured coupled dictionary learning

Images

Bic.

[11]

[12]

Proposed

Baboon

24.66

25.28

25.30

26.05

 

0.6359

0.7594

0.7602

0.7746

Boat

32.35

33.71

33.66

35.03

 

0.8989

0.9292

0.9291

0.9375

Bridge

26.49

27.46

27.46

28.36

 

0.7922

0.8445

0.8446

0.8725

Cameraman

26.32

27.63

27.61

28.73

 

0.8629

0.8918

0.8912

0.9132

Coala

33.40

36.26

36.26

37.83

 

0.8958

0.9513

0.9513

0.9697

Coastguard

29.13

30.47

30.47

31.61

 

0.7725

0.8495

0.8501

0.8731

Comic

26.05

28.33

28.28

28.78

 

0.8419

0.9105

0.9092

0.9255

Elaine

31.04

31.31

31.32

31.83

 

0.6531

0.7123

0.7131

0.7214

Face

34.74

36.53

36.53

36.90

 

0.8041

0.9095

0.9097

0.9432

Fingerprint

31.92

34.43

34.43

35.14

 

0.9513

0.9729

0.9730

0.9765

Flowers

30.41

32.19

32.10

33.47

 

0.8828

0.9270

0.9264

0.9496

Foreman

35.35

37.39

37.20

38.76

 

0.8928

0.9594

0.9586

0.9686

House

32.76

34.25

34.13

35.72

 

0.8928

0.9099

0.9092

0.9279

Lena

34.71

36.21

36.18

37.14

 

0.8507

0.9259

0.9260

0.9737

Man

29.25

30.38

30.33

31.40

 

0.8314

0.8782

0.8779

0.8905

Parrot

26.91

28.57

28.63

29.75

 

0.8931

0.9185

0.9186

0.9340

Average

30.34

31.90

31.87

32.91

 

0.8345

0.8906

0.8905

0.9095

  1. The data in boldface signifies highest value in comparison