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 1726–1750 of 7282 papers

TitleStatusHype
Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints—0
Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology—0
Denoising Lévy Probabilistic ModelsCode1
Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals—0
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
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
Lightweight Language-driven Grasp Detection using Conditional Consistency Model—0
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
Diffusion Models For Multi-Modal Generative Modeling—0
Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising—0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model—0
MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object DetectionCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation—0
Schrödinger Bridge for Generative Speech Enhancement—0
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising—0
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over TimeCode1
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
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—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