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 4576–4600 of 7282 papers

TitleStatusHype
A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond—0
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models—0
Practical Knowledge Distillation: Using DNNs to Beat DNNs—0
Practical Noise Simulation for RGB Images—0
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Une véritable approche _0 pour l'apprentissage de dictionnaire—0
Precise Phase Transition of Total Variation Minimization—0
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration—0
Predicting online user behaviour using deep learning algorithms—0
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising—0
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition—0
Prediction of Dynamical time Series Using Kernel Based Regression and Smooth Splines—0
Prediction of financial time series using LSTM and data denoising methods—0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Uni6Dv2: Noise Elimination for 6D Pose Estimation—0
Predictor Combination at Test Time—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning—0
Preparing fMRI Data for Statistical Analysis—0
Preserving Identity with Variational Score for General-purpose 3D Editing—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction—0
Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery—0
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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