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 1951–1975 of 7282 papers

TitleStatusHype
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems—0
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning—0
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model—0
Accelerating Video Diffusion Models via Distribution Matching—0
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy—0
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Denoising Hamiltonian Network for Physical Reasoning—0
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Addressing Negative Transfer in Diffusion Models—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Analysing Diffusion Segmentation for Medical Images—0
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space—0
Detection of blue whale vocalisations using a temporal-domain convolutional neural network—0
Bregman Plug-and-Play Priors—0
An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—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