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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 7282 papers

TitleStatusHype
Invariance Matters: Empowering Social Recommendation via Graph Invariant LearningCode1
Decoupled Diffusion Sparks Adaptive Scene Generation—0
Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis—0
Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge ModelsCode2
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark—0
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging—0
AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse—0
Computationally iterative methods for salt-and-pepper denoising—0
UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance—0
seg2med: a bridge from artificial anatomy to multimodal medical images—0
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization—0
Novel Diffusion Models for Multimodal 3D Hand Trajectory PredictionCode0
Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression—0
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems—0
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy—0
Zero-Shot Low-dose CT Denoising via Sinogram Flicking—0
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-ResolutionCode1
DDT: Decoupled Diffusion TransformerCode3
AstroClearNet: Deep image prior for multi-frame astronomical image restoration—0
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledgeCode0
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—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