Table 1 Comparison of quantitative indicators (PSNR and SSIM) with state-of-the-art methods on benchmark datasets.

From: SVTSR: image super-resolution using scattering vision transformer

Method

Scale

Training Dataset

Set5

Set14

BSD100

Urban100

Manga109

PSNR

SSIM

PSNR

SSIM

PSNR

SSIM

PSNR

SSIM

PSNR

SSIM

EDSR

\(\times 2\)

DIV2K

38.11

0.9602

33.92

0.9195

32.32

0.9013

32.93

0.9351

39.10

0.9773

RCAN

\(\times 2\)

DIV2K

38.27

0.9614

34.12

0.9216

32.41

0.9027

33.34

0.9384

39.44

0.9786

SAN

\(\times 2\)

DIV2K

38.31

0.9620

34.07

0.9213

32.42

0.9028

33.10

0.9370

39.32

0.9792

IGNN

\(\times 2\)

DIV2K

38.24

0.9613

34.07

0.9217

32.41

0.9025

33.23

0.9383

39.35

0.9786

HAN

\(\times 2\)

DIV2K

38.27

0.9614

34.16

0.9217

32.41

0.9027

33.35

0.9385

39.46

0.9785

NLSN

\(\times 2\)

DIV2K

38.34

0.9618

34.08

0.9231

32.43

0.9027

33.42

0.9394

39.59

0.9789

RCAN-it

\(\times 2\)

DF2K

38.37

0.9620

34.49

0.9250

32.48

0.9034

33.62

0.9410

39.88

0.9799

SwinIR

\(\times 2\)

DF2K

38.42

0.9623

34.46

0.9250

32.53

0.9041

33.81

0.9247

39.92

0.9797

EDT

\(\times 2\)

DF2K

38.45

0.9624

34.57

0.9258

32.52

0.9041

33.80

0.9425

39.93

0.9800

HAT

\(\times 2\)

DF2K

38.63

0.9630

34.86

0.9274

32.62

0.9053

33.45

0.9466

40.26

0.9809

SVTSR(ours)

\(\times 2\)

DF2K

38.69

0.9632

34.93

0.9281

32.71

0.9058

34.52

0.9472

40.34

0.9813

EDSR

\(\times 3\)

DIV2K

34.65

0.9280

30.52

0.8462

29.25

0.8093

28.80

0.8653

34.17

0.9476

RCAN

\(\times 3\)

DIV2K

34.74

0.9299

30.65

0.8482

29.32

0.8111

29.09

0.8702

34.44

0.9499

SAN

\(\times 3\)

DIV2K

34.75

0.9300

30.59

0.8476

29.33

0.8112

28.93

0.8671

34.30

0.9494

IGNN

\(\times 3\)

DIV2K

34.72

0.9298

30.66

0.8484

29.31

0.8105

29.03

0.8696

34.39

0.9496

HAN

\(\times 3\)

DIV2K

34.75

0.9299

30.67

0.8483

29.32

0.8110

29.10

0.8705

34.48

0.9500

NLSN

\(\times 3\)

DIV2K

34.85

0.9306

30.70

0.8485

29.34

0.8117

29.25

0.8726

34.57

0.9508

RCAN-it

\(\times 3\)

DF2K

34.86

0.9308

30.76

0.8505

29.39

0.8125

29.38

0.8755

34.92

0.9520

SwinIR

\(\times 3\)

DF2K

34.97

0.9318

30.93

0.8534

29.46

0.8145

29.75

0.8826

35.12

0.9537

EDT

\(\times 3\)

DF2K

34.97

0.9316

30.89

0.8527

29.44

0.8142

29.72

0.8814

35.13

0.9534

HAT

\(\times 3\)

DF2K

35.07

0.9329

31.08

0.8555

29.54

0.8167

30.23

0.8896

35.53

0.9552

SVTSR(ours)

\(\times 3\)

DF2K

35.14

0.9335

31.15

0.8559

29.60

0.8172

30.31

0.8902

35.61

0.9557

EDSR

\(\times 4\)

DIV2K

32.46

0.8968

28.80

0.7876

27.71

0.7420

26.64

0.8033

31.02

0.9148

RCAN

\(\times 4\)

DIV2K

32.63

0.9002

28.87

0.7889

27.77

0.7436

26.82

0.8087

31.22

0.9173

SAN

\(\times 4\)

DIV2K

32.64

0.9003

28.92

0.7888

27.78

0.7436

26.79

0.8068

31.18

0.9169

IGNN

\(\times 4\)

DIV2K

32.57

0.8998

28.85

0.7891

27.77

0.7434

26.84

0.8090

31.28

0.9182

HAN

\(\times 4\)

DIV2K

32.64

0.9002

28.90

0.7890

27.80

0.7442

26.85

0.8094

31.42

0.9177

NLSN

\(\times 4\)

DIV2K

32.59

0.9000

28.87

0.7891

27.78

0.7444

26.96

0.8109

31.27

0.9184

RCAN-it

\(\times 4\)

DF2K

32.69

0.9007

28.99

0.7922

27.87

0.7459

27.16

0.8168

31.78

0.9217

SwinIR

\(\times 4\)

DF2K

32.92

0.9044

29.09

0.7950

27.92

0.7489

27.45

0.8254

32.03

0.9260

EDT

\(\times 4\)

DF2K

32.82

0.9031

29.09

0.7939

27.91

0.7483

27.46

0.8246

32.05

0.9254

HAT

\(\times 4\)

DF2K

33.04

0.9056

29.23

0.7973

28.00

0.7517

27.97

0.8368

32.48

0.9292

SVTSR(ours)

\(\times 4\)

DF2K

33.17

0.9061

29.35

0.7978

28.13

0.7522

28.07

0.8372

32.59

0.9298

  1. The top three results are marked in bold, italic and bolditalic.