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 7026–7050 of 7282 papers

TitleStatusHype
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Image Restoration Using Deep Regulated Convolutional NetworksCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
Pushing The Limits of the Wiener Filter in Image DenoisingCode0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion ModelsCode0
WeakIdent: Weak formulation for Identifying Differential Equations using Narrow-fit and TrimmingCode0
DANAE: a denoising autoencoder for underwater attitude estimationCode0
Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy ChannelCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family ObservablesCode0
Image Segmentation by Iterative Inference from Conditional Score EstimationCode0
On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noiseCode0
On Inductive Biases That Enable Generalization of Diffusion TransformersCode0
Enhanced Control for Diffusion Bridge in Image RestorationCode0
Enhanced countering adversarial attacks via input denoising and feature restoringCode0
On Inference Stability for Diffusion ModelsCode0
Cross-Domain Conditional Diffusion Models for Time Series ImputationCode0
Image-to-Image MLP-mixer for Image ReconstructionCode0
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score MatchingCode0
Wide Inference Network for Image Denoising via Learning Pixel-distribution PriorCode0
Enhanced precision of circadian rhythm by output systemCode0
Pyramid Real Image Denoising NetworkCode0
Surgeon Style Fingerprinting and Privacy Risk Quantification via Discrete Diffusion Models in a Vision-Language-Action FrameworkCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Show:102550
← PrevPage 282 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