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 776–800 of 1459 papers

TitleStatusHype
Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition—0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
Decouple to Reconstruct: High Quality UHD Restoration via Active Feature Disentanglement and Reversible Fusion—0
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration—0
Deep Blind Image Inpainting—0
Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery—0
Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects—0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Deep Learning with Inaccurate Training Data for Image Restoration—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Deep Networks for Image Super-Resolution with Sparse Prior—0
Deep priors for satellite image restoration with accurate uncertainties—0
Deep Self-Dissimilarities as Powerful Visual Fingerprints—0
Deep Unrolled Network for Video Super-Resolution—0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis—0
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
Degrees of Freedom Analysis of Unrolled Neural Networks—0
Rethinking deinterlacing for early interlaced videos—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