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 4651–4675 of 7282 papers

TitleStatusHype
Single Cell Training on Architecture Search for Image Denoising—0
Learning to adapt unknown noise for hyperspectral image denoising—0
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising—0
Attention in a family of Boltzmann machines emerging from modern Hopfield networksCode0
Relationship Quantification of Image Degradations—0
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis—0
Denoising Self-attentive Sequential Recommendation—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
Complex-valued Retrievals From Noisy Images Using Diffusion Models—0
ADIR: Adaptive Diffusion for Image Reconstruction—0
Sources of Noise in Dialogue and How to Deal with Them—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
Neural Cell Video Synthesis via Optical-Flow Diffusion—0
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
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Interferometric Passive Radar Imaging with Deep Denoising Priors—0
DiffRF: Rendering-Guided 3D Radiance Field Diffusion—0
Fast Algorithm for Constrained Linear Inverse ProblemsCode0
Quantum median filter for Total Variation image denoising—0
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning—0
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer—0
Low-Rank Tensor Function Representation for Multi-Dimensional Data 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