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 1176–1200 of 1459 papers

TitleStatusHype
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Taming Diffusion Models for Image Restoration: A Review—0
Taming Generative Diffusion Prior for Universal Blind Image Restoration—0
TAPE: Task-Agnostic Prior Embedding for Image Restoration—0
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography—0
Task-GAN for Improved GAN based Image Restoration—0
Diffusion Model Compression for Image-to-Image Translation—0
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
Tensor Wiener Filter—0
Text-Aware Image Restoration with Diffusion Models—0
Text Extraction and Restoration of Old Handwritten Documents—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
The Perception-Robustness Tradeoff in Deterministic Image Restoration—0
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
The Power of Triply Complementary Priors for Image Compressive Sensing—0
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors—0
The structure of optimal parameters for image restoration problems—0
Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model—0
SPIRE: Semantic Prompt-Driven Image Restoration—0
To Recurse or not to Recurse,a Low Dose CT Study—0
Total Variation with Overlapping Group Sparsity and Lp Quasinorm for Infrared Image Deblurring under Salt-and-Pepper Noise—0
Toward Efficient Deep Blind RAW Image Restoration—0
Toward Interactive Modulation for Photo-Realistic Image Restoration—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Towards Context-aware Convolutional Network for Image Restoration—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