SOTAVerified

Image Restoration

Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).

Source: Blind Image Restoration without Prior Knowledge

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1326–1350 of 1459 papers

TitleStatusHype
NTIRE 2021 Challenge on Perceptual Image Quality Assessment—0
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results—0
NTIRE 2022 Challenge on Perceptual Image Quality Assessment—0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge—0
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge—0
NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge—0
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution—0
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment—0
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge—0
Object Tracking with Correlation Filters using Selective Single Background Patch—0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
On a Mechanism Framework of Autoencoders—0
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares—0
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing—0
One-Shot Image Restoration—0
On the Proof of Fixed-Point Convergence for Plug-and-Play ADMM—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Operation-wise Attention Network for Tampering Localization Fusion—0
Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits—0
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration—0
Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model—0
PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpening—0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
Patch-Based Image Restoration using Expectation Propagation—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
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All datasetsCDD-11UHDM.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OneRestoreAverage PSNR (dB)28.72—Unverified
2SRUDCAverage PSNR (dB)27.64—Unverified
3RestormerAverage PSNR (dB)26.99—Unverified
4WGWSNetAverage PSNR (dB)26.96—Unverified
5DGUNetAverage PSNR (dB)26.92—Unverified
6OKNetAverage PSNR (dB)26.33—Unverified
7MIRNetAverage PSNR (dB)25.97—Unverified
8PromptIRAverage PSNR (dB)25.9—Unverified
9MPRNetAverage PSNR (dB)25.47—Unverified
10MIRNetv2Average PSNR (dB)25.37—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR22.42—Unverified
2ESDNetPSNR22.12—Unverified
#ModelMetricClaimedVerifiedStatus
1730L37—Unverified