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Image Super Resolution
Image Super Resolution On Ffhq 256 X 256 4X
Image Super Resolution On Ffhq 256 X 256 4X
Metrics
FID
MS-SSIM
PSNR
SSIM
Results
Performance results of various models on this benchmark
Columns
Model Name
FID
MS-SSIM
PSNR
SSIM
Paper Title
Repository
FSRCNN
139.78
0.930
22.45
0.709
Accelerating the Super-Resolution Convolutional Neural Network
CAGFace
74.43
0.958
27.42
0.816
Component Attention Guided Face Super-Resolution Network: CAGFace
BDPM
5.71
-
30.05
0.864
Binary Diffusion Probabilistic Model
-
SRFBN
132.59
0.895
21.96
0.693
Feedback Network for Image Super-Resolution
BRGM
-
-
24.16
0.70
Bayesian Image Reconstruction using Deep Generative Models
PULSE
-
-
15.74
0.37
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
EnhanceNet
116.38
0.897
23.64
0.701
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
SRGAN
156.07
0.757
17.57
0.415
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
HiFaceGAN
5.36
0.971
28.65
0.816
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
SRCNN
147.21
0.900
23.12
0.688
Image Super-Resolution Using Deep Convolutional Networks
ESRGAN
166.36
0.747
15.43
0.267
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
EDSR
129.14
0.901
22.47
0.706
Enhanced Deep Residual Networks for Single Image Super-Resolution
0 of 12 row(s) selected.
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