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 1376–1400 of 7282 papers

TitleStatusHype
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM—0
Feature-guided score diffusion for sampling conditional densities—0
On the Effectiveness of Dataset Alignment for Fake Image DetectionCode1
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Data-Aware Training Quality Monitoring and Certification for Reliable Deep LearningCode0
DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis—0
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel PruningCode1
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory PredictionCode1
Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion ModelsCode2
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image EditingCode0
Variational Diffusion Posterior Sampling with Midpoint GuidanceCode1
Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems—0
Training-Free Adaptive Diffusion with Bounded Difference Approximation StrategyCode2
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
Reconstructive Visual Instruction TuningCode2
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities—0
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Conditional Lagrangian Wasserstein Flow for Time Series Imputation—0
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation—0
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction—0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
Show:102550
← PrevPage 56 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