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 526–550 of 1459 papers

TitleStatusHype
A Novel Variational Approach for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Variational Bayes image restoration with compressive autoencodersCode1
Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image RestorationCode1
Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition—0
Full-resolution MLPs Empower Medical Dense PredictionCode0
Joint Deep Image Restoration and Unsupervised Quality Assessment—0
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual NoiseCode1
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement—0
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion ModelCode1
Deep Equilibrium Diffusion Restoration with Parallel SamplingCode1
The Perception-Robustness Tradeoff in Deterministic Image Restoration—0
Dynamic Association Learning of Self-Attention and Convolution in Image Restoration—0
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences—0
Energy-Calibrated VAE with Test Time Free LunchCode0
Image Restoration via Frequency SelectionCode1
Deep Image Semantic Communication Model for Artificial Intelligent Internet of ThingsCode1
Pelvic floor MRI segmentation based on semi-supervised deep learning—0
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
Image Restoration with Point Spread Function Regularization and Active Learning—0
Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather RemovalCode1
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
Neural Degradation Representation Learning for All-In-One Image RestorationCode1
A Comparative Study of Image Restoration Networks for General Backbone Network DesignCode1
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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