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 151–175 of 7282 papers

TitleStatusHype
D-AR: Diffusion via Autoregressive ModelsCode2
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers—0
ZeroSep: Separate Anything in Audio with Zero Training—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models—0
Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple SclerosisCode0
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems—0
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample FilteringCode0
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples—0
PanoWan: Lifting Diffusion Video Generation Models to 360° with Latitude/Longitude-aware Mechanisms—0
Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models—0
Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification—0
Rhetorical Text-to-Image Generation via Two-layer Diffusion Policy Optimization—0
Plug-and-Play Posterior Sampling for Blind Inverse Problems—0
Shapley Value-driven Data Pruning for Recommender SystemsCode0
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Autoencoding Random Forests—0
MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes—0
Minute-Long Videos with Dual ParallelismsCode1
NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion—0
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly—0
Conditional Diffusion Models with Classifier-Free Gibbs-like GuidanceCode0
Show:102550
← PrevPage 7 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