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 626–650 of 1459 papers

TitleStatusHype
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy DataCode0
FRRffusion: Unveiling Authenticity with Diffusion-Based Face Retouching ReversalCode0
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and DesnowingCode0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Latent Convolutional ModelsCode0
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG^2+ Track 3Code0
Loss Functions for Neural Networks for Image ProcessingCode0
Crafting a Toolchain for Image Restoration by Deep Reinforcement LearningCode0
An Educated Warm Start For Deep Image Prior-Based Micro CT ReconstructionCode0
Attention Based Real Image RestorationCode0
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style TransferCode0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Intra and Inter Parser-Prompted Transformers for Effective Image RestorationCode0
Inverse problem regularization with hierarchical variational autoencodersCode0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image RestorationCode0
Fast Samplers for Inverse Problems in Iterative Refinement ModelsCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
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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