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 326–350 of 1459 papers

TitleStatusHype
Haze-Aware Attention Network for Single-Image Dehazing—0
In-Loop Filtering via Trained Look-Up Tables—0
MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration—0
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVCode2
Restoring Images in Adverse Weather Conditions via Histogram TransformerCode3
Exploring Richer and More Accurate Information via Frequency Selection for Image RestorationCode0
Region Attention Transformer for Medical Image RestorationCode1
Single-Image Shadow Removal Using Deep Learning: A Comprehensive SurveyCode3
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Robust Skin Color Driven Privacy Preserving Face Recognition via Function Secret Sharing—0
OneRestore: A Universal Restoration Framework for Composite DegradationCode3
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration—0
Zero-Shot Video Restoration and Enhancement Using Pre-Trained Image Diffusion ModelCode0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Blind Inversion using Latent Diffusion Priors—0
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
Improving Diffusion Inverse Problem Solving with Decoupled Noise AnnealingCode2
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Instruct-IPT: All-in-One Image Processing Transformer via Weight ModulationCode3
DiffLoss: unleashing diffusion model as constraint for training image restoration networkCode0
Rethinking and Defending Protective Perturbation in Personalized Diffusion ModelsCode1
ConStyle v2: A Strong Prompter for All-in-One Image RestorationCode1
Show:102550
← PrevPage 14 of 59Next →
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