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 6351–6375 of 7282 papers

TitleStatusHype
Segmentation-Aware Image Denoising without Knowing True SegmentationCode0
PCLD: Point Cloud Layerwise Diffusion for Adversarial PurificationCode0
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper ContaminationsCode0
Bilevel Learning with Inexact Stochastic GradientsCode0
Multimeasurement Generative ModelsCode0
Knowledge Enhanced Multi-intent Transformer Network for RecommendationCode0
Ultrasound Imaging based on the Variance of a Diffusion Restoration ModelCode0
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood PredictionCode0
DenoMAE: A Multimodal Autoencoder for Denoising Modulation SignalsCode0
Relational Autoencoder for Feature ExtractionCode0
Deep learning cardiac motion analysis for human survival predictionCode0
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel EstimationCode0
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document GenerationCode0
k-Sparse AutoencodersCode0
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging dataCode0
A Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence AssumptionCode0
Dense xUnit NetworksCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse SpikesCode0
SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging SegmentationCode0
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Releasing Differentially Private Event Logs Using Generative ModelsCode0
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance ImagesCode0
Perceptual Loss based Speech Denoising with an ensemble of Audio Pattern Recognition and Self-Supervised ModelsCode0
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic ModelCode0
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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