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 576–600 of 7282 papers

TitleStatusHype
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
Type Information-Assisted Self-Supervised Knowledge Graph DenoisingCode0
Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective—0
V2Edit: Versatile Video Diffusion Editor for Videos and 3D Scenes—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
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
PharMolixFM: All-Atom Foundation Models for Molecular Modeling and GenerationCode4
Accelerating Diffusion Sampling via Exploiting Local Transition Coherence—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
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language ModelsCode4
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial AttacksCode1
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
OminiControl2: Efficient Conditioning for Diffusion TransformersCode5
Posterior-Mean Denoising Diffusion Model for Realistic PET Image Reconstruction—0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Controlling Latent Diffusion Using Latent CLIPCode1
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Whiteness-based bilevel estimation of weighted TV parameter maps for image denoising—0
Illuminating Darkness: Enhancing Real-world Low-light Scenes with Smartphone ImagesCode1
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation—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