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 1226–1250 of 1459 papers

TitleStatusHype
Segmentation-Aware Image Denoising without Knowing True SegmentationCode0
Rethinking Atmospheric Turbulence Mitigation—0
Multi-level Encoder-Decoder Architectures for Image Restoration—0
Task-GAN for Improved GAN based Image Restoration—0
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
An approach to image denoising using manifold approximation without clean images—0
Path-Restore: Learning Network Path Selection for Image RestorationCode0
Automatic Temporally Coherent Video ColorizationCode0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps—0
A deep learning framework for quality assessment and restoration in video endoscopy—0
Lunar surface image restoration using U-net based deep neural networks—0
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial LearningCode0
CFSNet: Toward a Controllable Feature Space for Image RestorationCode0
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution—0
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Residual Non-local Attention Networks for Image RestorationCode0
Dual Residual Networks Leveraging the Potential of Paired Operations for Image RestorationCode0
Advanced Capsule Networks via Context Awareness—0
Proximal Splitting Networks for Image Restoration—0
Generalization of the Dark Channel Prior for Single Image Restoration—0
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation—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