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 901–925 of 1459 papers

TitleStatusHype
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression—0
Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration—0
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising—0
From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration—0
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
FSI: Frequency and Spatial Interactive Learning for Image Restoration in Under-Display Cameras—0
Functional Neural Networks for Parametric Image Restoration Problems—0
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond—0
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration—0
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content—0
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting—0
GenDeg: Diffusion-Based Degradation Synthesis for Generalizable All-in-One Image Restoration—0
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework—0
Generalization of the Dark Channel Prior for Single Image Restoration—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Generative Adversarial Network based on Resnet for Conditional Image Restoration—0
Generative Adversarial Network on Motion-Blur Image Restoration—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Generative Image Inpainting with Submanifold Alignment—0
Generative Latent Coding for Ultra-Low Bitrate Image Compression—0
Generic 3D Convolutional Fusion for image restoration—0
GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation—0
GLMHA A Guided Low-rank Multi-Head Self-Attention for Efficient Image Restoration and Spectral Reconstruction—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