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 851–900 of 1459 papers

TitleStatusHype
Recurrent Spike-based Image Restoration under General IlluminationCode0
Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image ReconstructionCode0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Benchmarking Ultra-High-Definition Image Reflection RemovalCode0
Decomposition Ascribed Synergistic Learning for Unified Image Restoration—0
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting—0
Phenotype-preserving metric design for high-content image reconstruction by generative inpaintingCode0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image RestorationCode0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Multiscale Coarse-to-Fine Guided Screenshot DemoiréingCode0
DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration—0
Latent Graph Attention for Enhanced Spatial Context—0
Dequantization and Color Transfer with Diffusion ModelsCode0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude—0
Cutting-Edge Techniques for Depth Map Super-Resolution—0
Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator—0
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal—0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse ProblemsCode0
Zero shot framework for satellite image restoration—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer—0
Counting Crowds in Bad Weather—0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep LearningCode0
Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse HalftoningCode0
A Dive into SAM Prior in Image Restoration—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go—0
Neural information coding for efficient spike-based image denoising—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG^2+ Track 3Code0
Mobile Image Restoration via Prior Quantization—0
CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the DarkCode0
Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A Minimalist Approach—0
Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse ProblemsCode0
Recovering Continuous Scene Dynamics from A Single Blurry Image with Events—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing—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