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

Showing 351375 of 7282 papers

TitleStatusHype
Training Diffusion Models with Reinforcement LearningCode2
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency ModelCode2
DocDiff: Document Enhancement via Residual Diffusion ModelsCode2
Collaborative Diffusion for Multi-Modal Face Generation and EditingCode2
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion ModelsCode2
An Edit Friendly DDPM Noise Space: Inversion and ManipulationsCode2
InterGen: Diffusion-based Multi-human Motion Generation under Complex InteractionsCode2
Diffusion Recommender ModelCode2
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image GenerationCode2
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
DDP: Diffusion Model for Dense Visual PredictionCode2
Implicit Diffusion Models for Continuous Super-ResolutionCode2
Masked Image Training for Generalizable Deep Image DenoisingCode2
3D Human Mesh Estimation from Virtual MarkersCode2
Leapfrog Diffusion Model for Stochastic Trajectory PredictionCode2
DiffIR: Efficient Diffusion Model for Image RestorationCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
Stochastic Interpolants: A Unifying Framework for Flows and DiffusionsCode2
DDFM: Denoising Diffusion Model for Multi-Modality Image FusionCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
KBNet: Kernel Basis Network for Image RestorationCode2
Unleashing Text-to-Image Diffusion Models for Visual PerceptionCode2
Human Motion Diffusion as a Generative PriorCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified