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 1076–1100 of 1459 papers

TitleStatusHype
Old Photo Restoration via Deep Latent Space TranslationCode3
Attention Cube Network for Image RestorationCode1
Blind Image Restoration with Flow Based Priors—0
Are Deep Neural Architectures Losing Information? Invertibility Is IndispensableCode1
Convolutional Nonlinear Dictionary with Cascaded Structure Filter Banks—0
Plug-and-Play Image Restoration with Deep Denoiser PriorCode1
Self-Organized Operational Neural Networks for Severe Image Restoration Problems—0
NAS-DIP: Learning Deep Image Prior with Neural Architecture SearchCode1
Deep Variational Network Toward Blind Image RestorationCode1
PNEN: Pyramid Non-Local Enhanced Networks—0
LIRA: Lifelong Image Restoration from Unknown Blended Distortions—0
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration—0
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results—0
Unsupervised Image Restoration Using Partially Linear DenoisersCode0
Deep Atrous Guided Filter for Image Restoration in Under Display CamerasCode1
Epigraphical Relaxation for Minimizing Layered Mixed Norms—0
Depth image denoising using nuclear norm and learning graph model—0
Rethinking Image Deraining via Rain Streaks and VaporsCode1
Wavelet-Based Dual-Branch Network for Image Demoiréing—0
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework—0
A regularized deep matrix factorized model of matrix completion for image restorationCode0
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Wavelet-Based Dual-Branch Network for Image DemoireingCode1
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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