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 1101–1150 of 1459 papers

TitleStatusHype
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
Fast & Robust Image Interpolation using Gradient Graph Laplacian Regularizer—0
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
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography—0
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
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
Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric—0
Rethinking deinterlacing for early interlaced videos—0
Learning to Sample the Most Useful Training Patches from Images—0
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks—0
Edge Adaptive Hybrid Regularization Model For Image Deblurring—0
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data—0
Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media with Airlight and Scattering Coefficient EstimationCode0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Generative and Discriminative Learning for Distorted Image Restoration—0
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data—0
Blind Motion Deblurring through SinGAN Architecture—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets—0
Transform Domain Pyramidal Dilated Convolution Networks For Restoration of Under Display Camera ImagesCode0
Blind Image Restoration with Flow Based Priors—0
Convolutional Nonlinear Dictionary with Cascaded Structure Filter Banks—0
Self-Organized Operational Neural Networks for Severe Image Restoration Problems—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration—0
LIRA: Lifelong Image Restoration from Unknown Blended Distortions—0
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results—0
Unsupervised Image Restoration Using Partially Linear DenoisersCode0
Epigraphical Relaxation for Minimizing Layered Mixed Norms—0
Depth image denoising using nuclear norm and learning graph model—0
Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework—0
Wavelet-Based Dual-Branch Network for Image Demoiréing—0
A regularized deep matrix factorized model of matrix completion for image restorationCode0
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring—0
A deep primal-dual proximal network for image restoration—0
Low-light Image Restoration with Short- and Long-exposure Raw Pairs—0
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategyCode0
Concatenated Attention Neural Network for Image Restoration—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