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 676–700 of 1459 papers

TitleStatusHype
Learning Continuous Face Representation with Explicit Functions—0
Deep Unrolled Network for Video Super-Resolution—0
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion—0
A Novel Variational Approach for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution—0
Improving Cross-modal Alignment for Text-Guided Image Inpainting—0
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation—0
Deep Self-Dissimilarities as Powerful Visual Fingerprints—0
Learning a Non-Blind Deblurring Network for Night Blurry Images—0
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration—0
Image Restoration with Point Spread Function Regularization and Active Learning—0
A Novel Framework for Image-to-image Translation and Image Compression—0
Image Restoration with Locally Selected Class-Adapted Models—0
Deep priors for satellite image restoration with accurate uncertainties—0
Blind Image Restoration with Flow Based Priors—0
Image Restoration using Total Variation with Overlapping Group Sparsity—0
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain—0
Image Restoration using Total Variation Regularized Deep Image Prior—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Blind Image Restoration without Prior Knowledge—0
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain—0
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models—0
Image Restoration using Feature-guidance—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer—0
Latent Neural Cellular Automata for Resource-Efficient 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