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 426–450 of 1459 papers

TitleStatusHype
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI ReconstructionCode1
Latent Neural Cellular Automata for Resource-Efficient Image Restoration—0
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration—0
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
Step-Calibrated Diffusion for Biomedical Optical Image RestorationCode0
DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen DomainsCode1
Multispectral Image Restoration by Generalized Opponent Transformation Total Variation—0
Divide-and-Conquer Posterior Sampling for Denoising Diffusion PriorsCode1
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
VmambaIR: Visual State Space Model for Image RestorationCode3
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion ModelCode2
A Spectrum-based Image Denoising Method with Edge Feature Enhancement—0
How Powerful Potential of Attention on Image Restoration?—0
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
D-YOLO a robust framework for object detection in adverse weather conditions—0
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted DataCode1
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network StructureCode1
Efficient Diffusion Model for Image Restoration by Residual ShiftingCode5
Boosting Image Restoration via Priors from Pre-trained Models—0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration—0
Generalizing to Out-of-Sample Degradations via Model ReprogrammingCode0
Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display CameraCode1
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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