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

Showing 251275 of 1459 papers

TitleStatusHype
Under-Display Camera Image Restoration with Scattering EffectCode1
Strip Attention for Image RestorationCode1
Physics-Driven Turbulence Image Restoration with Stochastic RefinementCode1
Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person RetrievalCode1
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution NetworkCode1
ProRes: Exploring Degradation-aware Visual Prompt for Universal Image RestorationCode1
Accelerating Multiframe Blind Deconvolution via Deep LearningCode1
TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image InpaintingCode1
Enlighten Anything: When Segment Anything Model Meets Low-Light Image EnhancementCode1
Illumination Controllable Dehazing Network based on Unsupervised Retinex EmbeddingCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata EmbeddingCode1
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image RestorationCode1
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather ConditionsCode1
UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and InterpolationCode1
WaveDM: Wavelet-Based Diffusion Models for Image RestorationCode1
Restore Anything Pipeline: Segment Anything Meets Image RestorationCode1
Reciprocal Attention Mixing Transformer for Lightweight Image RestorationCode1
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-SimilarityCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
A Mountain-Shaped Single-Stage Network for Accurate Image RestorationCode1
A Variational Perspective on Solving Inverse Problems with Diffusion ModelsCode1
IRNeXt: Rethinking Convolutional Network Design for Image RestorationCode1
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OneRestoreAverage PSNR (dB)28.72Unverified
2SRUDCAverage PSNR (dB)27.64Unverified
3RestormerAverage PSNR (dB)26.99Unverified
4WGWSNetAverage PSNR (dB)26.96Unverified
5DGUNetAverage PSNR (dB)26.92Unverified
6OKNetAverage PSNR (dB)26.33Unverified
7MIRNetAverage PSNR (dB)25.97Unverified
8PromptIRAverage PSNR (dB)25.9Unverified
9MPRNetAverage PSNR (dB)25.47Unverified
10MIRNetv2Average PSNR (dB)25.37Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR22.42Unverified
2ESDNetPSNR22.12Unverified
#ModelMetricClaimedVerifiedStatus
1730L37Unverified