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 3026–3050 of 7282 papers

TitleStatusHype
Generalization in diffusion models arises from geometry-adaptive harmonic representationsCode1
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF—0
On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring—0
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards UpsamplingCode0
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review—0
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising—0
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants—0
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
JPEG Information Regularized Deep Image Prior for Denoising—0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of DiffusionCode2
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
A Novel U-Net Architecture for Denoising of Real-world Noise Corrupted Phonocardiogram Signal—0
Diffusion Posterior Illumination for Ambiguity-aware Inverse RenderingCode1
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation—0
Denoising Diffusion Bridge ModelsCode2
Toward Universal Speech Enhancement for Diverse Input Conditions—0
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics—0
Stochastic Digital Twin for Copy Detection Patterns—0
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models—0
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
Space-Time Attention with Shifted Non-Local Search—0
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI—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