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 3126–3150 of 7282 papers

TitleStatusHype
Robust Online Classification: From Estimation to Denoising—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Are We Using Autoencoders in a Wrong Way?Code0
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systemsCode1
Diffusion Models with Deterministic Normalizing Flow PriorsCode0
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction—0
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography—0
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models—0
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model—0
Latent Painter—0
Holistic Processing of Colour Images Using Novel Quaternion-Valued Wavelets on the Plane—0
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRICode1
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation—0
Textual and Visual Prompt Fusion for Image Editing via Step-Wise AlignmentCode0
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising ModelsCode1
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models—0
DiffBIR: Towards Blind Image Restoration with Generative Diffusion PriorCode4
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models—0
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
1st Place Solution for the 5th LSVOS Challenge: Video Instance SegmentationCode1
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image ReconstructionCode1
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local SmoothingCode0
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspectiveCode0
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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