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

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Showing 701–725 of 1459 papers

TitleStatusHype
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Training-Free Large Model Priors for Multiple-in-One Image Restoration—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Haze-Aware Attention Network for Single-Image Dehazing—0
MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration—0
In-Loop Filtering via Trained Look-Up Tables—0
Exploring Richer and More Accurate Information via Frequency Selection for Image RestorationCode0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Robust Skin Color Driven Privacy Preserving Face Recognition via Function Secret Sharing—0
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
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
DiffLoss: unleashing diffusion model as constraint for training image restoration networkCode0
Diffusion Model-based FOD Restoration from High Distortion in dMRI—0
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt GuidanceCode0
Diffusion-Based Adaptation for Classification of Unknown Degraded ImagesCode0
3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement—0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
Diffusion-based image inpainting with internal learningCode0
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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