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 1001–1050 of 1459 papers

TitleStatusHype
PD-GAN: Probabilistic Diverse GAN for Image InpaintingCode1
A Refined Inertial DC Algorithm for DC Programming—0
Dynamic Image Restoration and Fusion Based on Dynamic Degradation—0
Region-Adaptive Deformable Network for Image Quality AssessmentCode1
Posterior Sampling for Image Restoration using Explicit Patch PriorsCode0
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection NetworkCode1
Interpretable Unsupervised Diversity Denoising and Artefact RemovalCode1
Image Desaturation for SDO/AIA Using Deep Learning—0
Training a Task-Specific Image Reconstruction Loss—0
Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors—0
Single Underwater Image Restoration by Contrastive LearningCode1
COLA-Net: Collaborative Attention Network for Image RestorationCode1
Perceptual Image Restoration with High-Quality Priori and Degradation Learning—0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks—0
Feature-Align Network with Knowledge Distillation for Efficient Denoising—0
Accelerating Large Kernel Convolutions with Nested Winograd Transformation.pdf—0
Deep Unrolled Network for Video Super-Resolution—0
A GAN-Based Input-Size Flexibility Model for Single Image Dehazing—0
Plug-and-Play gradient-based denoisers applied to CT image enhancementCode0
Learning local regularization for variational image restoration—0
Learning to Enhance Visual Quality via Hyperspectral Domain Mapping—0
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression—0
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery—0
Image Restoration by Deep Projected GSURE—0
Multi-Stage Progressive Image RestorationCode1
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
Exploiting Raw Images for Real-Scene Super-ResolutionCode1
Non-uniform Blur Kernel Estimation via Adaptive Basis DecompositionCode1
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer—0
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
Adaptively Sparse Regularization for Blind Image Restoration—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
BANet: Blur-aware Attention Networks for Dynamic Scene DeblurringCode1
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography—0
GAN Inversion: A SurveyCode2
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity ConstraintCode1
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising—0
Data augmentation for deep learning based accelerated MRI reconstruction—0
Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning—0
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
Time-Travel RephotographyCode1
Underwater image filtering: methods, datasets and evaluation—0
Searching for Controllable Image Restoration Networks—0
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CTCode0
DILIE: Deep Internal Learning for Image Enhancement—0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
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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