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 1201–1250 of 1459 papers

TitleStatusHype
Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration—0
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences—0
Invert2Restore: Zero-Shot Degradation-Blind Image Restoration—0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
ISP-Agnostic Image Reconstruction for Under-Display Cameras—0
JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration—0
Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations—0
Joint Deep Image Restoration and Unsupervised Quality Assessment—0
Joint Face Image Restoration and Frontalization for Recognition—0
JPEG Information Regularized Deep Image Prior for Denoising—0
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Key-Graph Transformer for Image Restoration—0
KNN Local Attention for Image Restoration—0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning—0
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
LiDAR Depth Map Guided Image Compression Model—0
Lightweight single-image super-resolution network based on dual paths—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation—0
LIRA: Lifelong Image Restoration from Unknown Blended Distortions—0
LL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model—0
LLMRA: Multi-modal Large Language Model based Restoration Assistant—0
Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection—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