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 301–325 of 1459 papers

TitleStatusHype
Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image RestorationCode1
Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision TasksCode1
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method—0
Wavelet based inpainting detection—0
MultiColor: Image Colorization by Learning from Multiple Color Spaces—0
Physical prior guided cooperative learning framework for joint turbulence degradation estimation and infrared video restoration—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
Dual High-Order Total Variation Model for Underwater Image RestorationCode0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Restore Anything Model via Efficient Degradation AdaptationCode1
Training-Free Large Model Priors for Multiple-in-One Image Restoration—0
Attention-Guided Low-Rank Tensor CompletionCode1
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—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