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 1301–1325 of 1459 papers

TitleStatusHype
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
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
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
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination—0
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary SearchCode0
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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