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 2101–2125 of 7282 papers

TitleStatusHype
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction—0
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience—0
Invariant Risk Minimization Is A Total Variation ModelCode0
Investigating Self-Supervised Image Denoising with Denaturation—0
A text-based, generative deep learning model for soil reflectance spectrum simulation in the VIS-NIR (400-2499 nm) bandsCode1
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image DenoisingCode2
LocInv: Localization-aware Inversion for Text-Guided Image EditingCode2
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph DiffusionCode2
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Streamlining Image Editing with Layered Diffusion Brushes—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)—0
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models—0
TheaterGen: Character Management with LLM for Consistent Multi-turn Image GenerationCode2
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising—0
Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image TransmissionCode1
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification—0
Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion ModelCode1
Show:102550
← PrevPage 85 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