Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic
  Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image
restoration method that harnesses generative prior and the power of model
scaling up. Leveraging multi-modal techniques and advanced generative prior,
SUPIR marks a significant advance in intelligent and realistic image
restoration. As a pivotal catalyst within SUPIR, model scaling dramatically
enhances its capabilities and demonstrates new potential for image restoration.
We collect a dataset comprising 20 million high-resolution, high-quality images
for model training, each enriched with descriptive text annotations. SUPIR
provides the capability to restore images guided by textual prompts, broadening
its application scope and potential. Moreover, we introduce negative-quality
prompts to further improve perceptual quality. We also develop a
restoration-guided sampling method to suppress the fidelity issue encountered
in generative-based restoration. Experiments demonstrate SUPIR's exceptional
restoration effects and its novel capacity to manipulate restoration through
textual prompts.
65b1ef73b4225e3e4235abd9
Fanghua Yu
65b1ef73b4225e3e4235abda
Jinjin Gu
65b1ef73b4225e3e4235abdb
Zheyuan Li
65b1ef73b4225e3e4235abdc
Jinfan Hu
admin_assigned
2024-02-09T14:02:28.165Z
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Jinfan_Hu
user
jfhu
65b1ef73b4225e3e4235abdd
Xiangtao Kong
65b1ef73b4225e3e4235abde
Xintao Wang
admin_assigned
2024-01-25T08:19:01.494Z
60e272ca6c78a8c122b12127
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Xintao Wang
user
Xintao
65b1ef73b4225e3e4235abdf
Jingwen He
claimed_verified
2024-09-11T15:01:43.704Z
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jwhe
user
jwhejwhe
65b1ef73b4225e3e4235abe0
Yu Qiao
65b1ef73b4225e3e4235abe1
Chao Dong
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic
  Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image
restoration method that harnesses generative prior and the power of model
scaling up. Leveraging multi-modal techniques and advanced generative prior,
SUPIR marks a significant advance in intelligent and realistic image
restoration. As a pivotal catalyst within SUPIR, model scaling dramatically
enhances its capabilities and demonstrates new potential for image restoration.
We collect a dataset comprising 20 million high-resolution, high-quality images
for model training, each enriched with descriptive text annotations. SUPIR
provides the capability to restore images guided by textual prompts, broadening
its application scope and potential. Moreover, we introduce negative-quality
prompts to further improve perceptual quality. We also develop a
restoration-guided sampling method to suppress the fidelity issue encountered
in generative-based restoration. Experiments demonstrate SUPIR's exceptional
restoration effects and its novel capacity to manipulate restoration through
textual prompts.
SUPIR, an advanced image restoration method combining generative prior and model scaling, achieves high-quality results and flexible restoration guided by textual prompts.
generative prior model scaling multi-modal techniques image restoration high-resolution images descriptive text annotations textual prompts negative-quality prompts restoration-guided sampling generative-based restoration