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 4051–4075 of 7282 papers

TitleStatusHype
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis—0
Relationship Quantification of Image Degradations—0
FSID: Fully Synthetic Image Denoising via Procedural Scene GenerationCode4
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Neural Cell Video Synthesis via Optical-Flow Diffusion—0
Sources of Noise in Dialogue and How to Deal with Them—0
DiffusionInst: Diffusion Model for Instance SegmentationCode2
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
Complex-valued Retrievals From Noisy Images Using Diffusion Models—0
Image Inpainting via Iteratively Decoupled Probabilistic ModelingCode1
Score-based denoising for atomic structure identificationCode1
Domino Denoise: An Accurate Blind Zero-Shot Denoiser using Domino Tilings—0
PhysDiff: Physics-Guided Human Motion Diffusion Model—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Interferometric Passive Radar Imaging with Deep Denoising Priors—0
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Quantum median filter for Total Variation image denoising—0
Fast Algorithm for Constrained Linear Inverse ProblemsCode0
DiffRF: Rendering-Guided 3D Radiance Field Diffusion—0
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer—0
Linear Combinations of Patches are Unreasonably Effective for Single-Image DenoisingCode0
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels—0
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning—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