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 2251–2275 of 7282 papers

TitleStatusHype
MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice—0
Generative Modeling of Seismic Data using Diffusion Models and its Application to Multi-Purpose Seismic Inverse ProblemsCode0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias—0
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning—0
Building 3D In-Context Learning Universal Model in NeuroimagingCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution—0
Controllable Motion Generation via Diffusion Modal CouplingCode0
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage—0
Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery—0
Denoising Functional Maps: Diffusion Models for Shape Correspondence—0
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging—0
FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation—0
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation ModelsCode0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Periodic Materials Generation using Text-Guided Joint Diffusion ModelCode0
Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image EnhancementCode0
DiffBrush:Just Painting the Art by Your Hands—0
Denoising bivariate signals via smoothing and polarization priors—0
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts—0
SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization—0
Spectral Analysis of Representational Similarity with Limited Neurons—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—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