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 2451–2500 of 7282 papers

TitleStatusHype
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
Stochastic Localization via Iterative Posterior SamplingCode1
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion ModelCode1
DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image PersonalizationCode2
Classification Diffusion Models: Revitalizing Density Ratio Estimation—0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Patch-based adaptive temporal filter and residual evaluation—0
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement—0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
PFCM: Poisson flow consistency models for low-dose CT image denoising—0
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator—0
Target Score Matching—0
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy BiasesCode1
Color Image Denoising Using The Green Channel PriorCode1
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Rolling Diffusion Models—0
Computationally efficient reductions between some statistical models—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
An attempt to generate new bridge types from latent space of denoising diffusion Implicit modelCode0
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Particle Denoising Diffusion SamplerCode1
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Social Physics Informed Diffusion Model for Crowd SimulationCode1
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—0
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Time Series Diffusion in the Frequency DomainCode2
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
Blue noise for diffusion modelsCode2
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Context-Aware Automated Passenger Counting Data Denoising—0
Denoising Diffusion Probabilistic Models in Six Simple Steps—0
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal DenoisingCode1
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
Unified Discrete Diffusion for Categorical DataCode1
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models—0
The last Dance : Robust backdoor attack via diffusion models and bayesian approach—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
Denoising Diffusion via Image-Based Rendering—0
DexDiffuser: Generating Dexterous Grasps with Diffusion Models—0
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells—0
A Lennard-Jones Layer for Distribution Normalization—0
ViewFusion: Learning Composable Diffusion Models for Novel View SynthesisCode1
Diffusive Gibbs SamplingCode1
Guidance with Spherical Gaussian Constraint for Conditional DiffusionCode2
Denoising Time Cycle Modeling for Recommendation—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