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 276–300 of 1459 papers

TitleStatusHype
Taming Diffusion Models for Image Restoration: A Review—0
Quaternion Nuclear Norm minus Frobenius Norm Minimization for color image reconstruction—0
PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpening—0
AgileIR: Memory-Efficient Group Shifted Windows Attention for Agile Image Restoration—0
Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement—0
Lightweight single-image super-resolution network based on dual paths—0
Power Line Aerial Image Restoration under dverse Weather: Datasets and BaselinesCode0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
Data-free Distillation with Degradation-prompt Diffusion for Multi-weather Image Restoration—0
F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring—0
Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language ModelsCode1
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting—0
Accurate Forgetting for All-in-One Image Restoration Model—0
AWRaCLe: All-Weather Image Restoration using Visual In-Context Learning—0
Efficient Image Restoration through Low-Rank Adaptation and Stable Diffusion XL—0
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content—0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image RestorationCode2
A Preliminary Exploration Towards General Image Restoration—0
CODE: Confident Ordinary Differential EditingCode0
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts RemovalCode1
Taming Generative Diffusion Prior for Universal Blind Image Restoration—0
Harnessing Multi-resolution and Multi-scale Attention for Underwater Image RestorationCode0
Multi-Scale Representation Learning for Image Restoration with State-Space Model—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