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 76–100 of 1459 papers

TitleStatusHype
Efficient Visual State Space Model for Image DeblurringCode2
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image RestorationCode2
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image DenoisingCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement TaskCode2
Bracketing Image Restoration and Enhancement with High-Low Frequency DecompositionCode2
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image DenoisingCode2
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image RestorationCode2
Omni-Kernel Network for Image RestorationCode2
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion ModelCode2
Dual-domain strip attention for image restorationCode2
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
U-shaped Vision Mamba for Single Image DehazingCode2
CascadedGaze: Efficiency in Global Context Extraction for Image RestorationCode2
Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning StrategyCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image RestorationCode2
Controlling Vision-Language Models for Multi-Task Image RestorationCode2
Residual Denoising Diffusion ModelsCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
PromptIR: Prompting for All-in-One Blind Image RestorationCode2
Low-Light Image Enhancement with Wavelet-based Diffusion ModelsCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion ModelsCode2
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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