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 3801–3825 of 7282 papers

TitleStatusHype
Learning to See Low-Light Images via Feature Domain Adaptation—0
VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera NetworkCode0
Compensation Sampling for Improved Convergence in Diffusion ModelsCode0
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration—0
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning—0
A Note on the Convergence of Denoising Diffusion Probabilistic Models—0
Consistency Models for Scalable and Fast Simulation-Based InferenceCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation—0
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model—0
MVDD: Multi-View Depth Diffusion Models—0
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
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Approximate Caching for Efficiently Serving Diffusion Models—0
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution—0
PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play—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
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction—0
Adaptive Multi-step Refinement Network for Robust Point Cloud RegistrationCode0
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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