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 2751–2775 of 7282 papers

TitleStatusHype
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
MVDD: Multi-View Depth Diffusion Models—0
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation—0
Prompt-In-Prompt Learning for Universal Image RestorationCode1
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting—0
Resolution Chromatography of Diffusion Models—0
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIsCode1
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play—0
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within TransformerCode1
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human GenerationCode1
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Approximate Caching for Efficiently Serving Diffusion Models—0
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution—0
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation—0
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—0
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention—0
Training Neural Networks on RAW and HDR Images for Restoration TasksCode1
TokenCompose: Text-to-Image Diffusion with Token-level SupervisionCode1
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
Adaptive Multi-step Refinement Network for Robust Point Cloud RegistrationCode0
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context LearningCode1
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction—0
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object DetectionCode1
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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