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 4351–4375 of 7282 papers

TitleStatusHype
Sampling, Diffusions, and Stochastic Localization—0
Data Augmentation for Diverse Voice Conversion in Noisy Environments—0
GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework—0
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural networkCode0
CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising—0
Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT—0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency—0
Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?—0
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise ModelingCode0
Adapting Sentence Transformers for the Aviation Domain—0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Neural information coding for efficient spike-based image denoising—0
Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts—0
On enhancing the robustness of Vision Transformers: Defensive DiffusionCode0
Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning MethodsCode0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation—0
Automated Data Denoising for Recommendation—0
Diffusion-based Signal Refiner for Speech Separation—0
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models—0
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
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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