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 326–350 of 1459 papers

TitleStatusHype
Deep Image Semantic Communication Model for Artificial Intelligent Internet of ThingsCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Enlighten Anything: When Segment Anything Model Meets Low-Light Image EnhancementCode1
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image RestorationCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
Ab-initio Contrast Estimation and Denoising of Cryo-EM ImagesCode1
Annular Computational Imaging: Capture Clear Panoramic Images through Simple LensCode1
Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image RestorationCode1
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed ImagesCode1
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
Learning Distortion Invariant Representation for Image Restoration from A Causality PerspectiveCode1
Learning from History: Task-agnostic Model Contrastive Learning for Image RestorationCode1
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image RestorationCode1
Cross Aggregation Transformer for Image RestorationCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional NetworksCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Learning to Deblur using Light Field Generated and Real Defocus ImagesCode1
Blind Inverse Gamma Correction with Maximized Differential EntropyCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Attentive Fine-Grained Structured Sparsity for Image RestorationCode1
EnlightenGAN: Deep Light Enhancement without Paired SupervisionCode1
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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