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 451–500 of 1459 papers

TitleStatusHype
Stochastic Frequency Masking to Improve Super-Resolution and Denoising NetworksCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
Deep Blind Video Super-resolutionCode1
Replacing Mobile Camera ISP with a Single Deep Learning ModelCode1
Total Deep Variation for Linear Inverse ProblemsCode1
Scale-wise Convolution for Image RestorationCode1
Fully Trainable and Interpretable Non-Local Sparse Models for Image RestorationCode1
Scientific Image Restoration AnywhereCode1
Deformable Kernel Networks for Joint Image FilteringCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and BetterCode1
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image RepresentationsCode1
EnlightenGAN: Deep Light Enhancement without Paired SupervisionCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy imagesCode1
The 2018 PIRM Challenge on Perceptual Image Super-resolutionCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Noise2Noise: Learning Image Restoration without Clean DataCode1
Deep Image PriorCode1
Image Restoration by Iterative Denoising and Backward ProjectionsCode1
Recurrent Inference Machines for Solving Inverse ProblemsCode1
On-Demand Learning for Deep Image RestorationCode1
Unsupervised Part Discovery via Descriptor-Based Masked Image Restoration with Optimized ConstraintsCode0
Double-Diffusion: Diffusion Conditioned Diffusion Probabilistic Model For Air Quality Prediction—0
Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration—0
Wild refitting for black box prediction—0
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs—0
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance—0
Reversing Flow for Image Restoration—0
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior—0
Optimization-Based Image Restoration under Implementation Constraints in Optical Analog Circuits—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
Text-Aware Image Restoration with Diffusion Models—0
M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration—0
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI—0
UniRes: Universal Image Restoration for Complex Degradations—0
SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training—0
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge—0
NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution—0
Image Restoration Learning via Noisy Supervision in the Fourier Domain—0
EquiReg: Equivariance Regularized Diffusion for Inverse Problems—0
Reference-Guided Identity Preserving Face Restoration—0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
Manifold-aware Representation Learning for Degradation-agnostic Image Restoration—0
Dual Ascent Diffusion for Inverse Problems—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration—0
Breaking Complexity Barriers: High-Resolution Image Restoration with Rank Enhanced Linear Attention—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