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 2201–2250 of 7282 papers

TitleStatusHype
Weighted Graph Structure Learning with Attention Denoising for Node ClassificationCode0
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting—0
Noise Synthesis for Low-Light Image Denoising with Diffusion Models—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Are Deep Speech Denoising Models Robust to Adversarial Noise?—0
Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information—0
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video EnhancementCode0
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing—0
Type Information-Assisted Self-Supervised Knowledge Graph DenoisingCode0
ROODI: Reconstructing Occluded Objects with Denoising Inpainters—0
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
VideoMerge: Towards Training-free Long Video Generation—0
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation—0
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging—0
CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems—0
V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes—0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective—0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising—0
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation—0
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Posterior-Mean Denoising Diffusion Model for Realistic PET Image Reconstruction—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
Denoising Hamiltonian Network for Physical Reasoning—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping—0
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution—0
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images—0
Exposure Bias Reduction for Enhancing Diffusion Transformer Feature CachingCode0
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation—0
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation—0
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation—0
Federated Learning for Diffusion Models—0
Speech Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder—0
ProSE: Diffusion Priors for Speech Enhancement—0
D3DR: Lighting-Aware Object Insertion in Gaussian Splatting—0
Diffusion Model Based Probabilistic Day-ahead Load Forecasting—0
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation—0
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion ModelsCode0
MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice—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