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 1301–1350 of 1459 papers

TitleStatusHype
Identifying Recurring Patterns with Deep Neural Networks for Natural Image DenoisingCode0
Non-Local Recurrent Network for Image RestorationCode0
Multi-Scale Weighted Nuclear Norm Image Restoration—0
Image Restoration by Estimating Frequency Distribution of Local Patches—0
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration—0
Multi-level Wavelet-CNN for Image RestorationCode0
Object Tracking with Correlation Filters using Selective Single Background Patch—0
Moiré Photo Restoration Using Multiresolution Convolutional Neural NetworksCode0
Multi-Scale Face Restoration with Sequential Gating Ensemble Network—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Simultaneous Fidelity and Regularization Learning for Image RestorationCode0
Crafting a Toolchain for Image Restoration by Deep Reinforcement LearningCode0
Supervised Convolutional Sparse Coding—0
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging ProblemsCode0
A Cascaded Convolutional Neural Network for Single Image Dehazing—0
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration—0
Unveiling the invisible - mathematical methods for restoring and interpreting illuminated manuscriptsCode0
Noise2Noise: Learning Image Restoration without Clean DataCode1
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary SearchCode0
Deep Residual Network for Joint Demosaicing and Super-ResolutionCode0
Image denoising with generalized Gaussian mixture model patch priors—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Near-lossless _-constrained Image Decompression via Deep Neural Network—0
Graph Spectral Image Processing—0
Enhancing Underwater Imagery using Generative Adversarial NetworksCode0
Deep Blind Image Inpainting—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Deep Image PriorCode1
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
The Perception-Distortion TradeoffCode0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Image Restoration by Iterative Denoising and Backward ProjectionsCode1
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition—0
Depth and Image Restoration From Light Field in a Scattering Medium—0
Learning Discriminative Data Fitting Functions for Blind Image Deblurring—0
Learning to Super-Resolve Blurry Face and Text Images—0
Deep Mean-Shift Priors for Image RestorationCode0
A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
A wavelet frame coefficient total variational model for image restoration—0
MemNet: A Persistent Memory Network for Image RestorationCode0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception ModuleCode0
Generative Adversarial Network based on Resnet for Conditional Image Restoration—0
Impulsive noise removal from color images with morphological filtering—0
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition—0
Learning Dynamic Guidance for Depth Image Enhancement—0
Recurrent Inference Machines for Solving Inverse ProblemsCode1
Image Restoration from Patch-based Compressed Sensing Measurement—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