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 1251–1300 of 1459 papers

TitleStatusHype
Privacy-Preserving Action Recognition using Coded Aperture Videos—0
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy imagesCode1
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Bridging the Gap Between Computational Photography and Visual Recognition—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
Image Super-Resolution as a Defense Against Adversarial AttacksCode0
I Can See Clearly Now : Image Restoration via De-Raining—0
Total Variation with Overlapping Group Sparsity and Lp Quasinorm for Infrared Image Deblurring under Salt-and-Pepper Noise—0
Residual Dense Network for Image RestorationCode0
Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks—0
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face RestorationCode0
Evolutionary Neural Architecture Search for Image Restoration—0
Deep Unfolding of a Proximal Interior Point Method for Image RestorationCode0
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined DistortionsCode0
Why Are Deep Representations Good Perceptual Quality Features?—0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Dense xUnit NetworksCode0
Deep Network Interpolation for Continuous Imagery Effect TransitionCode0
Deep Learning with Inaccurate Training Data for Image Restoration—0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Self-Refining Deep Symmetry Enhanced Network for Rain RemovalCode0
Visual Attention Network for Low Dose CT—0
Image Restoration using Total Variation Regularized Deep Image Prior—0
Neural Nearest Neighbors NetworksCode0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Deep Learning-Based Channel EstimationCode0
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy DataCode0
Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network—0
Active image restoration—0
The 2018 PIRM Challenge on Perceptual Image Super-resolutionCode1
Non-blind Image Restoration Based on Convolutional Neural Network—0
Reconstruction-based Pairwise Depth Dataset for Depth Image Enhancement Using CNN—0
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks—0
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective—0
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)—0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
Physics-Based Generative Adversarial Models for Image Restoration and Beyond—0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation—0
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding—0
Learning Generic Diffusion Processes for Image Restoration—0
Vehicle Image Generation Going Well with The Surroundings—0
External Patch-Based Image Restoration Using Importance Sampling—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Improved Techniques for Learning to Dehaze and Beyond: A Collective StudyCode0
Latent Convolutional ModelsCode0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—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