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 5326–5350 of 7282 papers

TitleStatusHype
Artifact reduction for separable non-local means—0
Simple Contrastive Graph Clustering—0
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement—0
A salt and pepper noise image denoising method based on the generative classification—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations—0
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models—0
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity—0
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables—0
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
A Semantic Modular Framework for Events Topic Modeling in Social Media—0
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration—0
ASIC Implementation of Denoising Filters for Pacemakers—0
A Simple Analysis of Discretization Error in Diffusion Models—0
A New Method for Removing the Moire' Pattern from Images—0
SimpleStyle: An Adaptable Style Transfer Approach—0
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina—0
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter—0
A Simple Sparse Denoising Layer for Robust Deep 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