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

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Showing 6501–6550 of 7282 papers

TitleStatusHype
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion ModelCode0
Considering Image Information and Self-similarity: A Compositional Denoising NetworkCode0
Residual Non-local Attention Networks for Image RestorationCode0
Deep Class Aware DenoisingCode0
Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal BoundsCode0
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local SmoothingCode0
Murine AI excels at cats and cheese: Structural differences between human and mouse neurons and their implementation in generative AIsCode0
Time Step Generating: A Universal Synthesized Deepfake Image DetectorCode0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
SNRGAN: The Semi Noise Reduction GAN for Image DenoisingCode0
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved GeneralizationCode0
Learning-Based Reconstruction of FRI SignalsCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography imagesCode0
Adversarial Regularizers in Inverse ProblemsCode0
Statistical Component Separation for Targeted Signal Recovery in Noisy MixturesCode0
CLMB: deep contrastive learning for robust metagenomic binningCode0
[Re] Spatial-Adaptive Network for Single Image DenoisingCode0
VConv-DAE: Deep Volumetric Shape Learning Without Object LabelsCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image ProcessingCode0
Generative Flows as a General Purpose Solution for Inverse ProblemsCode0
Unconditional Diffusion for Generative Sequential RecommendationCode0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
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