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 301–350 of 1459 papers

TitleStatusHype
Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image RestorationCode1
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision TasksCode1
Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method—0
Wavelet based inpainting detection—0
Physical prior guided cooperative learning framework for joint turbulence degradation estimation and infrared video restoration—0
MultiColor: Image Colorization by Learning from Multiple Color Spaces—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
Contribution-based Low-Rank Adaptation with Pre-training Model for Real Image Restoration—0
A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition—0
UniProcessor: A Text-induced Unified Low-level Image ProcessorCode1
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image RestorationCode1
Dilated Strip Attention Network for Image Restoration—0
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models—0
CLII: Visual-Text Inpainting via Cross-Modal Predictive Interaction—0
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Dual High-Order Total Variation Model for Underwater Image RestorationCode0
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
Restore Anything Model via Efficient Degradation AdaptationCode1
Training-Free Large Model Priors for Multiple-in-One Image Restoration—0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Attention-Guided Low-Rank Tensor CompletionCode1
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
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
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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