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

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Showing 2201–2250 of 7282 papers

TitleStatusHype
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
A Machine Learning Imaging Core using Separable FIR-IIR Filters—0
Dehazing Ultrasound using Diffusion Models—0
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations—0
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
A Machine Learning Approach for Denoising and Upsampling HRTFs—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix—0
Efficient Video and Audio processing with Loihi 2—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation—0
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval—0
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI—0
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Alternating minimization for a single step TV-Stokes model for image denoising—0
Adaptive Image Denoising by Mixture Adaptation—0
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification—0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation—0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
Defending Against Adversarial Iris Examples Using Wavelet Decomposition—0
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement—0
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data—0
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
-VAEs : Optimising variational inference by learning data-dependent divergence skew—0
Deep Unfolding with Normalizing Flow Priors for Inverse Problems—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising—0
Deep Ultrasound Denoising Without Clean Data—0
Deep Ultrasound Denoising Using Diffusion Probabilistic Models—0
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI—0
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
Bayesian Inference for Neighborhood Filters With Application in Denoising—0
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors—0
Deep Stacked Networks with Residual Polishing for Image Inpainting—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
A Low-Cost & Real-Time Motion Capture System—0
Bayesian Formulations for Graph Spectral Denoising—0
Deep Speech Denoising with Vector Space Projections—0
Deep Spectral Prior—0
Bayesian ensemble learning for image denoising—0
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms—0
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds—0
Bayesian ECG reconstruction using denoising diffusion generative models—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