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 626–650 of 1459 papers

TitleStatusHype
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration—0
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization—0
AgileIR: Memory-Efficient Group Shifted Windows Attention for Agile Image Restoration—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
DiffGAR: Model-Agnostic Restoration from Generative Artifacts Using Image-to-Image Diffusion Models—0
DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior—0
Breaking Complexity Barriers: High-Resolution Image Restoration with Rank Enhanced Linear Attention—0
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement—0
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences—0
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM—0
Joint Deep Image Restoration and Unsupervised Quality Assessment—0
Latent Neural Cellular Automata for Resource-Efficient Image Restoration—0
A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition—0
In-Loop Filtering via Trained Look-Up Tables—0
BP-DIP: A Backprojection based Deep Image Prior—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification—0
Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal—0
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—0
A Geometric Approach to Color Image Regularization—0
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs—0
Depth image denoising using nuclear norm and learning graph model—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
Boosting Image Restoration via Priors from Pre-trained 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