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 876–900 of 1459 papers

TitleStatusHype
A Trained Regularization Approach Based on Born Iterative Method for Electromagnetic Imaging—0
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Autoencoder-based holographic image restoration—0
A Validation Approach to Over-parameterized Matrix and Image Recovery—0
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise—0
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model—0
A wavelet frame coefficient total variational model for image restoration—0
AWRaCLe: All-Weather Image Restoration using Visual In-Context Learning—0
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations—0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
Beyond Pixels: Text Enhances Generalization in Real-World Image Restoration—0
Binary Diffusion Probabilistic Model—0
Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A Minimalist Approach—0
Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving—0
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer—0
Blind Image Restoration with Flow Based Priors—0
Blind Image Restoration without Prior Knowledge—0
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion—0
Blind Inversion using Latent Diffusion Priors—0
Blind Motion Deblurring through SinGAN Architecture—0
Blind Underwater Image Restoration using Co-Operational Regressor Networks—0
Blind Visible Watermark Removal with Morphological Dilation—0
Block based refitting in _12 sparse regularisation—0
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration—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