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 1201–1225 of 1459 papers

TitleStatusHype
Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration—0
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences—0
Invert2Restore: Zero-Shot Degradation-Blind Image Restoration—0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
ISP-Agnostic Image Reconstruction for Under-Display Cameras—0
JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration—0
Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations—0
Joint Deep Image Restoration and Unsupervised Quality Assessment—0
Joint Face Image Restoration and Frontalization for Recognition—0
JPEG Information Regularized Deep Image Prior for Denoising—0
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Key-Graph Transformer for Image Restoration—0
KNN Local Attention for Image Restoration—0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning—0
L0TV: A New Method for Image Restoration in the Presence of Impulse Noise—0
Latent Graph Attention for Enhanced Spatial Context—0
LatentINDIGO: An INN-Guided Latent Diffusion Algorithm for Image Restoration—0
Latent Neural Cellular Automata for Resource-Efficient Image Restoration—0
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent—0
Learning a Non-Blind Deblurring Network for Night Blurry Images—0
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Learning Continuous Face Representation with Explicit Functions—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