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 3351–3375 of 7282 papers

TitleStatusHype
Learning Mixtures of Gaussians Using the DDPM Objective—0
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion ModelsCode0
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution—0
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion ModelsCode1
MissDiff: Training Diffusion Models on Tabular Data with Missing Values—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation—0
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion FunctionalsCode0
Unsupervised Coordinate-Based Video Denoising—0
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising—0
Weighted Anisotropic-Isotropic Total Variation for Poisson DenoisingCode0
Counting Guidance for High Fidelity Text-to-Image Synthesis—0
Graph Denoising Diffusion for Inverse Protein FoldingCode1
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar—0
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation—0
ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models—0
Spiking Denoising Diffusion Probabilistic ModelsCode1
SVNR: Spatially-variant Noise Removal with Denoising Diffusion—0
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack—0
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in RadiotherapyCode1
Face Morphing Attack Detection with Denoising Diffusion Probabilistic ModelsCode0
Statistical Component Separation for Targeted Signal Recovery in Noisy MixturesCode0
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis—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