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 3126–3150 of 7282 papers

TitleStatusHype
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion ModelsCode0
Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints—0
Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals—0
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images—0
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
Lightweight Language-driven Grasp Detection using Conditional Consistency Model—0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
Diffusion Models For Multi-Modal Generative Modeling—0
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising—0
qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model—0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation—0
FAST-GSC: Fast and Adaptive Semantic Transmission for Generative Semantic Communication—0
Estimating Probability Densities with Transformer and Denoising DiffusionCode0
Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems—0
Schrödinger Bridge for Generative Speech Enhancement—0
MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise—0
RGB2Point: 3D Point Cloud Generation from Single RGB Images—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