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Image Super Resolution On Ffhq 256 X 256 4X

Métriques

FID

MS-SSIM

PSNR

SSIM

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	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

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