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 651–675 of 1459 papers

TitleStatusHype
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
Deep Image Restoration For Image Anti-ForensicsCode0
Image Reconstruction via Deep Image Prior SubspacesCode0
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined DistortionsCode0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Convergent Primal-Dual Plug-and-Play Image Restoration: A General Algorithm and ApplicationsCode0
Improved Techniques for Learning to Dehaze and Beyond: A Collective StudyCode0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality AssessmentCode0
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse ProblemsCode0
Probabilistic Residual Learning for Aleatoric Uncertainty in Image RestorationCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
Extremely Low-light Image Enhancement with Scene Text RestorationCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal—0
Controllable Image Restoration for Under-Display Camera in Smartphones—0
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
What's in the Image? Explorable Decoding of Compressed Images—0
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—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