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 831–840 of 1459 papers

TitleStatusHype
Unifying Image Processing as Visual Prompting Question Answering—0
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression—0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis—0
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image RestorationCode0
A Restoration Network as an Implicit Prior—0
JPEG Information Regularized Deep Image Prior for Denoising—0
Guided Frequency Loss for Image Restoration—0
Uncertainty Quantification via Neural Posterior Principal Components—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
License Plate Super-Resolution Using Diffusion 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