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 851–875 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
Show:102550
← PrevPage 35 of 59Next →
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