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 576–600 of 1459 papers

TitleStatusHype
Blind Visible Watermark Removal with Morphological Dilation—0
Shape from Semantics: 3D Shape Generation from Multi-View Semantics—0
Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration—0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
CDI: Blind Image Restoration Fidelity Evaluation based on Consistency with Degraded Image—0
INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration—0
Binary Diffusion Probabilistic Model—0
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner—0
Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects—0
FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image RestorationCode0
UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior—0
Proxies for Distortion and Consistency with Applications for Real-World Image Restoration—0
SILO: Solving Inverse Problems with Latent Operators—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration—0
Convergent Primal-Dual Plug-and-Play Image Restoration: A General Algorithm and ApplicationsCode0
Sea-ing in Low-lightCode0
ACL: Activating Capability of Linear Attention for Image Restoration—0
Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering—0
Consistency Posterior Sampling for Diverse Image Synthesis—0
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
Dual Prompting Image Restoration with Diffusion Transformers—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