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Table 1 Analysis on hierarchical features

From: Sparse self-attention aggregation networks for neural sequence slice interpolation

Extractor

cremi_triplet A

cremi_triplet B

cremi_triplet C

mouse_triplet

 

PSNR

SSIM

PSNR

SSIM

PSNR

SSIM

IE

PSNR

SSIM

IE

U-Net [40]

18.10

0.4295

16.75

0.3549

16.26

0.3542

28.82

14.59

0.2219

36.55

SU-Net

18.12

0.4352

16.71

0.3417

16.18

0.3280

29.71

14.75

0.2089

35.99

RDN [32]

17.73

0.4448

16.59

0.3521

16.44

0.3451

28.75

14.85

0.2195

35.53

SRDN(ours)

18.26

0.4374

16.79

0.3712

16.46

0.3575

28.38

15.04

0.2156

34.81

  1. We compared the actual effects of different feature extractors on the cremi_triplet datasets and mouse_triplet dataset. Lower IEs indicates better performance