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 2201–2225 of 7282 papers

TitleStatusHype
Weighted Graph Structure Learning with Attention Denoising for Node ClassificationCode0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Noise Synthesis for Low-Light Image Denoising with Diffusion Models—0
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting—0
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing—0
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video EnhancementCode0
Are Deep Speech Denoising Models Robust to Adversarial Noise?—0
Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information—0
ROODI: Reconstructing Occluded Objects with Denoising Inpainters—0
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging—0
VideoMerge: Towards Training-free Long Video Generation—0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes—0
Type Information-Assisted Self-Supervised Knowledge Graph DenoisingCode0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation—0
Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective—0
CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systems—0
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation—0
Noise2Score3D: Tweedie's Approach for Unsupervised Point Cloud Denoising—0
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
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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