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 3551–3575 of 7282 papers

TitleStatusHype
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework—0
Sampling, Diffusions, and Stochastic Localization—0
FLIGHT Mode On: A Feather-Light Network for Low-Light Image EnhancementCode1
Data Augmentation for Diverse Voice Conversion in Noisy Environments—0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—0
Uncertainty Guided Label Denoising for Document-level Distant Relation ExtractionCode1
Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency—0
Pyramid Diffusion Models For Low-light Image EnhancementCode1
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural networkCode0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising—0
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized AttentionCode2
Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT—0
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-SimilarityCode1
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise ModelingCode0
AR-Diffusion: Auto-Regressive Diffusion Model for Text GenerationCode1
Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?—0
Adapting Sentence Transformers for the Aviation Domain—0
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models—0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
Discrete Diffusion Probabilistic Models for Symbolic Music GenerationCode1
Neural information coding for efficient spike-based image denoising—0
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts—0
Toward Moiré-Free and Detail-Preserving Demosaicking—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