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 1011–1020 of 1459 papers

TitleStatusHype
TAPE: Task-Agnostic Prior Embedding for Image Restoration—0
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging—0
Alternative design of DeepPDNet in the context of image restoration—0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
Tensor Wiener Filter—0
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality AssessmentCode0
Fast and High-Quality Image Denoising via Malleable Convolutions—0
KNN Local Attention for Image Restoration—0
GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation—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