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
BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata EmbeddingCode1
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse ProblemsCode0
Zero shot framework for satellite image restoration—0
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image RestorationCode1
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer—0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Counting Crowds in Bad Weather—0
Low-Light Image Enhancement with Wavelet-based Diffusion ModelsCode2
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep LearningCode0
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather ConditionsCode1
Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse HalftoningCode0
UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and InterpolationCode1
WaveDM: Wavelet-Based Diffusion Models for Image RestorationCode1
A Dive into SAM Prior in Image Restoration—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Restore Anything Pipeline: Segment Anything Meets Image RestorationCode1
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
Reciprocal Attention Mixing Transformer for Lightweight Image RestorationCode1
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-SimilarityCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go—0
Neural information coding for efficient spike-based image denoising—0
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
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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