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 1126–1150 of 1459 papers

TitleStatusHype
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network—0
Self-Organized Operational Neural Networks for Severe Image Restoration Problems—0
Self-Organized Residual Blocks for Image Super-Resolution—0
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems—0
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
Self-supervised Hyperspectral Image Restoration using Separable Image Prior—0
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising—0
Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semi-supervised atmospheric component learning in low-light image problem—0
Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models—0
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation—0
Shape from Semantics: 3D Shape Generation from Multi-View Semantics—0
Shrinkage Fields for Effective Image Restoration—0
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers—0
SIDL: A Real-World Dataset for Restoring Smartphone Images with Dirty Lenses—0
SILO: Solving Inverse Problems with Latent Operators—0
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing—0
Single Cell Training on Architecture Search for Image Denoising—0
Single Image Restoration for Participating Media Based on Prior Fusion—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
Snow Mask Guided Adaptive Residual Network for Image Snow Removal—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features—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