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 1101–1150 of 7282 papers

TitleStatusHype
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis—0
OmniCreator: Self-Supervised Unified Generation with Universal Editing—0
On the Feature Learning in Diffusion Models—0
NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training—0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
An overview of diffusion models for generative artificial intelligence—0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation—0
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision—0
Advanced Video Inpainting Using Optical Flow-Guided Efficient DiffusionCode3
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured SignalsCode0
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout GenerationCode1
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty EstimationCode1
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian SplattingCode2
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy—0
MoTe: Learning Motion-Text Diffusion Model for Multiple Generation Tasks—0
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient NetworkCode0
FiRe: Fixed-points of Restoration Priors for Solving Inverse ProblemsCode0
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution—0
Timestep Embedding Tells: It's Time to Cache for Video Diffusion ModelCode1
Random Sampling for Diffusion-based Adversarial PurificationCode0
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation—0
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising—0
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference—0
Towards a Mechanistic Explanation of Diffusion Model Generalization—0
Limit Order Book Event Stream Prediction with Diffusion Model—0
Unpacking the Individual Components of Diffusion Policy—0
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation—0
Towards Chunk-Wise Generation for Long Videos—0
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion ModelsCode0
MatchDiffusion: Training-free Generation of Match-cutsCode1
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject GenerationCode1
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Omegance: A Single Parameter for Various Granularities in Diffusion-Based SynthesisCode2
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral ConstraintsCode2
StableAnimator: High-Quality Identity-Preserving Human Image AnimationCode5
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Reward Incremental Learning in Text-to-Image Generation—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—0
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
Can LLMs be Good Graph Judge for Knowledge Graph Construction?Code1
Mixed-State Quantum Denoising Diffusion Probabilistic Model—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
NovelGS: Consistent Novel-view Denoising via Large Gaussian Reconstruction Model—0
Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation—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