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 1151–1175 of 1459 papers

TitleStatusHype
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint—0
Solving QVIs for Image Restoration with Adaptive Constraint Sets—0
Space-adaptive anisotropic bivariate Laplacian regularization for image restoration—0
Space-variant TV regularization for image restoration—0
Sparse Representation of a Blur Kernel for Blind Image Restoration—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
Speckle Image Restoration without Clean Data—0
Speckle Reduction with Trained Nonlinear Diffusion Filtering—0
Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering—0
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework—0
STAIC regularization for spatio-temporal image reconstruction—0
Star-Net: Improving Single Image Desnowing Model With More Efficient Connection and Diverse Feature Interaction—0
Statistical Analysis of Loopy Belief Propagation in Random Fields—0
Stereo Image Rain Removal via Dual-View Mutual Attention—0
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement—0
Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning—0
Structured and Localized Image Restoration—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting—0
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction—0
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Supervised Convolutional Sparse Coding—0
Support Driven Wavelet Frame-based Image Deblurring—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—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