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 1401–1425 of 1459 papers

TitleStatusHype
Image restoration quality assessment based on regional differential information entropy—0
Relationship Quantification of Image Degradations—0
RELD: Regularization by Latent Diffusion Models for Image Restoration—0
Removing the Representation Error of GAN Image Priors Using the Deep Decoder—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Report on UG^2+ Challenge Track 1: Assessing Algorithms to Improve Video Object Detection and Classification from Unconstrained Mobility Platforms—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Residual Swin Transformer Channel Attention Network for Image Demosaicing—0
Restoration by Generation with Constrained Priors—0
Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting—0
Restoration of Images with Wavefront Aberrations—0
Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator—0
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models—0
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems—0
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go—0
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning—0
Rethinking Atmospheric Turbulence Mitigation—0
Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information—0
Rethinking RGB Color Representation for Image Restoration Models—0
Rethinking Video Frame Interpolation from Shutter Mode Induced Degradation—0
RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images—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