Image Super Resolution On Div2K Val 4X

Metrics

LPIPS

PSNR

SSIM

Results

Performance results of various models on this benchmark

Model Name	LPIPS	PSNR	SSIM	Paper Title
LINF	0.112	27.33	0.76	Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
EDSR	-	29.25	0.9017	Enhanced Deep Residual Networks for Single Image Super-Resolution
RankSRGAN	-	26.55	0.75	Implicit Diffusion Models for Continuous Super-Resolution
GOUB	0.220	26.89	0.7478	Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
FxSR-PD t=0.8	0.1028	27.51	0.789	Flexible Style Image Super-Resolution using Conditional Objective
LAR-SR	-	27.03	0.77	Implicit Diffusion Models for Continuous Super-Resolution
Bicubic	-	26.7	0.77	Implicit Diffusion Models for Continuous Super-Resolution
LINF t=0.0	0.248	29.14	0.83	Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
HCFlow	-	27.02	0.76	Implicit Diffusion Models for Continuous Super-Resolution
LIIF	-	29	0.89	Learning Continuous Image Representation with Local Implicit Image Function
HCFlow++	-	26.61	0.74	Implicit Diffusion Models for Continuous Super-Resolution
SRFlow-LP	0.109	27.51	0.78	Boosting Flow-based Generative Super-Resolution Models via Learned Prior
ESRGAN	-	26.22	0.75	Implicit Diffusion Models for Continuous Super-Resolution
PixelRL-SR	-	28.08	0.8140	Multi-Step Reinforcement Learning for Single Image Super-Resolution
LINF-LP	0.105	28.00	0.78	Boosting Flow-based Generative Super-Resolution Models via Learned Prior
AESOP	0.0893	29.137	0.8023	Auto-Encoded Supervision for Perceptual Image Super-Resolution
RCOT	0.104	28.41	0.804	Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
SRFlow	0.12	27.09	0.76	SRFlow: Learning the Super-Resolution Space with Normalizing Flow
IDM	-	27.59	0.78	Implicit Diffusion Models for Continuous Super-Resolution
FxSR-PD t=0.0	0.239	29.24	0.8383	Flexible Style Image Super-Resolution using Conditional Objective

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