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 1801–1825 of 7282 papers

TitleStatusHype
Mesh Denoising with Facet Graph ConvolutionsCode0
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser NetworkCode0
Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning MethodsCode0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Blind Universal Bayesian Image Denoising with Gaussian Noise Level LearningCode0
Blind Restoration of Real-World Audio by 1D Operational GANsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder networkCode0
Analysis of learning a flow-based generative model from limited sample complexityCode0
Medical image denoising using convolutional denoising autoencodersCode0
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
MCRAGE: Synthetic Healthcare Data for FairnessCode0
Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family ObservablesCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score MatchingCode0
Missing Data Imputation with Adversarially-trained Graph Convolutional NetworksCode0
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE DiscoveryCode0
Masked Diffusion with Task-awareness for Procedure Planning in Instructional VideosCode0
Bivariate Matrix-valued Linear Regression (BMLR): Finite-sample performance under Identifiability and Sparsity AssumptionsCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Mask-GVAE: Blind Denoising Graphs via PartitionCode0
An adaptive denoising recommendation algorithm for causal separation biasCode0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
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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