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 1026–1050 of 1459 papers

TitleStatusHype
L0TV: A New Method for Image Restoration in the Presence of Impulse Noise—0
Latent Graph Attention for Enhanced Spatial Context—0
LatentINDIGO: An INN-Guided Latent Diffusion Algorithm for Image Restoration—0
Latent Neural Cellular Automata for Resource-Efficient Image Restoration—0
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent—0
Learning a Non-Blind Deblurring Network for Night Blurry Images—0
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Learning Continuous Face Representation with Explicit Functions—0
Learning Diffusion Texture Priors for Image Restoration—0
Learning Discriminative Data Fitting Functions for Blind Image Deblurring—0
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration—0
Learning Dynamic Guidance for Depth Image Enhancement—0
Learning Enriched Features for Fast Image Restoration and Enhancement—0
Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration—0
Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis—0
Learning Generic Diffusion Processes for Image Restoration—0
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Learning local regularization for variational image restoration—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning to Enhance Visual Quality via Hyperspectral Domain Mapping—0
Learning to Sample the Most Useful Training Patches from Images—0
Learning to Super-Resolve Blurry Face and Text Images—0
Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions—0
License Plate Super-Resolution Using Diffusion Models—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