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 6526–6550 of 7282 papers

TitleStatusHype
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene SynthesisCode0
Restoration based Generative ModelsCode0
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative DenoisingCode0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Generative Modeling of Seismic Data using Diffusion Models and its Application to Multi-Purpose Seismic Inverse ProblemsCode0
Nasal Patches and Curves for Expression-robust 3D Face RecognitionCode0
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold RobustnessCode0
Generative Modeling with DiffusionCode0
ConsistencyDet: A Few-step Denoising Framework for Object Detection Using the Consistency ModelCode0
Learning Dynamics of Linear Denoising AutoencodersCode0
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challengesCode0
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation StudyCode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video EnhancementCode0
Generative Plug and Play: Posterior Sampling for Inverse ProblemsCode0
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous SystemsCode0
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization FrameworkCode0
VideoPure: Diffusion-based Adversarial Purification for Video RecognitionCode0
Learning Equations from Biological Data with Limited Time SamplesCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
NCP: Neural Correspondence Prior for Effective Unsupervised Shape MatchingCode0
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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