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 1051–1075 of 1459 papers

TitleStatusHype
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Empowering Image Recovery_ A Multi-Attention Approach—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Epigraphical Relaxation for Minimizing Layered Mixed Norms—0
EquiReg: Equivariance Regularized Diffusion for Inverse Problems—0
Equivariant Denoisers for Image Restoration—0
ERL-Net: Entangled Representation Learning for Single Image De-Raining—0
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity—0
Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging—0
Evolutionary Neural Architecture Search for Image Restoration—0
Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
What's in the Image? Explorable Decoding of Compressed Images—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration—0
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal—0
ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring—0
External Patch-Based Image Restoration Using Importance Sampling—0
F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring—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