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 641–650 of 1459 papers

TitleStatusHype
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