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 201–225 of 1459 papers

TitleStatusHype
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Equivariant Denoisers for Image Restoration—0
ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration—0
Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian NoiseCode4
Aberration Correcting Vision Transformers for High-Fidelity Metalens Imaging—0
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration—0
Deep priors for satellite image restoration with accurate uncertainties—0
LL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model—0
Blind Underwater Image Restoration using Co-Operational Regressor Networks—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image RestorationCode3
Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and BeyondCode1
Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration—0
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion—0
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-ResolutionCode3
Adaptive Blind All-in-One Image RestorationCode1
Hierarchical Information Flow for Generalized Efficient Image Restoration—0
Complexity Experts are Task-Discriminative Learners for Any Image Restoration—0
Low-rank Adaptation-based All-Weather Removal for Autonomous Navigation—0
GenDeg: Diffusion-Based Degradation Synthesis for Generalizable All-in-One Image Restoration—0
MWFormer: Multi-Weather Image Restoration Using Degradation-Aware TransformersCode2
Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions—0
Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding—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