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 251–275 of 1459 papers

TitleStatusHype
DREB-Net: Dual-stream Restoration Embedding Blur-feature Fusion Network for High-mobility UAV Object DetectionCode1
An Intelligent Agentic System for Complex Image Restoration ProblemsCode2
LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image RestorationCode2
A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future TrendsCode3
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond—0
Analysis and Benchmarking of Extending Blind Face Image Restoration to Videos—0
Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image RestorersCode1
TANet: Triplet Attention Network for All-In-One Adverse Weather Image RestorationCode1
InstantIR: Blind Image Restoration with Instant Generative Reference—0
ReFIR: Grounding Large Restoration Models with Retrieval AugmentationCode2
Learning Efficient and Effective Trajectories for Differential Equation-based Image RestorationCode2
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse ProblemsCode0
Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model—0
Diffusion State-Guided Projected Gradient for Inverse ProblemsCode1
PnP-Flow: Plug-and-Play Image Restoration with Flow MatchingCode2
Posterior sampling via Langevin dynamics based on generative priors—0
GLMHA A Guided Low-rank Multi-Head Self-Attention for Efficient Image Restoration and Spectral Reconstruction—0
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image RestorationCode4
A Survey on Diffusion Models for Inverse Problems—0
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank AdaptationCode0
Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image RestorationCode2
Toward Efficient Deep Blind RAW Image Restoration—0
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEsCode1
PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language InstructionsCode3
Denoising diffusion models for high-resolution microscopy image restoration—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