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 476–500 of 1459 papers

TitleStatusHype
Residual Dense Network for Image RestorationCode0
Residual Non-local Attention Networks for Image RestorationCode0
Removal of speckle noises from ultrasound images using five different deep learning networksCode0
ERetinex: Event Camera Meets Retinex Theory for Low-Light Image EnhancementCode0
Recurrent Spike-based Image Restoration under General IlluminationCode0
Conditional Injective Flows for Bayesian ImagingCode0
Enhancing Underwater Imagery using Generative Adversarial NetworksCode0
Enhancing Image Restoration through Learning Context-Rich and Detail-Accurate FeaturesCode0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 ClassificationCode0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Energy-Calibrated VAE with Test Time Free LunchCode0
Progressive Reconstruction of Visual Structure for Image InpaintingCode0
Emphasizing Crucial Features for Efficient Image RestorationCode0
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative TransformerCode0
Probabilistic Residual Learning for Aleatoric Uncertainty in Image RestorationCode0
PMS-Net: Robust Haze Removal Based on Patch Map for Single ImagesCode0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
Plug-and-Play gradient-based denoisers applied to CT image enhancementCode0
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image ProcessingCode0
Posterior Sampling for Image Restoration using Explicit Patch PriorsCode0
Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel EstimationCode0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
CODE: Confident Ordinary Differential EditingCode0
Phenotype-preserving metric design for high-content image reconstruction by generative inpaintingCode0
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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