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 1851–1875 of 7282 papers

TitleStatusHype
Blind Image Super-Resolution with Spatial Context Hallucination—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Modified Weibull distribution for Biomedical signals denoising—0
Blind Image Restoration with Flow Based Priors—0
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up—0
Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction—0
Blind Image Denoising via Dependent Dirichlet Process Tree—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition—0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Denoising Linear Models with Permuted Data—0
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet—0
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network—0
Denoising Induction Motor Sounds Using an Autoencoder—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Denoising Low-dose Images Using Deep Learning of Time Series Images—0
Blind Denoising Autoencoder—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Denoising Improves Latent Space Geometry in Text 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