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 6351–6400 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
Classifier and Exemplar Synthesis for Zero-Shot LearningCode0
From neural PCA to deep unsupervised learningCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
From Patches to Images: A Nonparametric Generative ModelCode0
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise ModelingCode0
A Semisupervised Approach for Language Identification based on Ladder NetworksCode0
You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient ShortcutsCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
Single Stage Adaptive Multi-Attention Network for Image RestorationCode0
Using Autoencoders on Differentially Private Federated Learning GANsCode0
Zero-shot denoising via neural compression: Theoretical and algorithmic frameworkCode0
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive DenoisingCode0
Language-Aware Multilingual Machine Translation with Self-Supervised LearningCode0
Detecting Patch Adversarial Attacks with Image ResidualsCode0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
Multi-Object Self-Supervised Depth DenoisingCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Binding via Reconstruction ClusteringCode0
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
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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