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 1351–1375 of 7282 papers

TitleStatusHype
The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion—0
Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers—0
TabSeq: A Framework for Deep Learning on Tabular Data via Sequential OrderingCode1
DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone—0
FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial ModelingCode0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-ExplorationCode0
Label-free prediction of fluorescence markers in bovine satellite cells using deep learning—0
Stable Diffusion with Continuous-time Neural Network—0
On the Relation Between Linear Diffusion and Power Iteration—0
Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR—0
One Step Diffusion via Shortcut ModelsCode4
MING: A Functional Approach to Learning Molecular Generative Models—0
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation—0
Open Materials 2024 (OMat24) Inorganic Materials Dataset and ModelsCode1
Constrained Posterior Sampling: Time Series Generation with Hard Constraints—0
SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video GenerationCode2
Synthetic Augmentation for Anatomical Landmark Localization using DDPMs—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
A Survey on Deep Tabular Learning—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
Feature-guided score diffusion for sampling conditional densities—0
Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method—0
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement—0
On the Effectiveness of Dataset Alignment for Fake Image DetectionCode1
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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