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 4001–4025 of 7282 papers

TitleStatusHype
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF—0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Learning to Reach Goals via DiffusionCode0
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
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants—0
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising—0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
JPEG Information Regularized Deep Image Prior for Denoising—0
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
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation—0
Toward Universal Speech Enhancement for Diverse Input Conditions—0
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
Object Motion Guided Human Motion Synthesis—0
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models—0
Space-Time Attention with Shifted Non-Local Search—0
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising—0
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI—0
Stochastic Digital Twin for Copy Detection Patterns—0
DeepPCR: Parallelizing Sequential Operations in Neural Networks—0
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics—0
Distilling ODE Solvers of Diffusion Models into Smaller Steps—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