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 481–490 of 1459 papers

TitleStatusHype
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration—0
Equivariant Denoisers for Image Restoration—0
EquiReg: Equivariance Regularized Diffusion for Inverse Problems—0
Consistency Posterior Sampling for Diverse Image Synthesis—0
AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations—0
Epigraphical Relaxation for Minimizing Layered Mixed Norms—0
Conformal and Low-Rank Sparse Representation for Image Restoration—0
Concatenated Attention Neural Network for Image Restoration—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing—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