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 1001–1025 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
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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