SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1175 of 7282 papers

TitleStatusHype
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk ControlCode1
Information-Theoretic DiffusionCode1
DDM^2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion ModelsCode1
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by EnhancingCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
Stable Target Field for Reduced Variance Score Estimation in Diffusion ModelsCode1
Optimizing DDPM Sampling with Shortcut Fine-TuningCode1
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion ModelsCode1
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion RestorationCode1
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue CloudsCode1
Don't Play Favorites: Minority Guidance for Diffusion ModelsCode1
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory PredictionCode1
Input Perturbation Reduces Exposure Bias in Diffusion ModelsCode1
Mixed Attention Network for Hyperspectral Image DenoisingCode1
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation EquationCode1
Solving Inverse Physics Problems with Score MatchingCode1
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintsCode1
Fast Inference in Denoising Diffusion Models via MMD FinetuningCode1
The role of noise in denoising models for anomaly detection in medical imagesCode1
Representing Noisy Image Without DenoisingCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language ModelsCode1
WIRE: Wavelet Implicit Neural RepresentationsCode1
Task-specific Scene Structure RepresentationsCode1
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data SetsCode1
Show:102550
← PrevPage 47 of 292Next →
All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified