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 3926–3950 of 7282 papers

TitleStatusHype
Successfully Applying Lottery Ticket Hypothesis to Diffusion ModelCode0
MCRAGE: Synthetic Healthcare Data for FairnessCode0
Direct Unsupervised Denoising—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
Style Description based Text-to-Speech with Conditional Prosodic Layer Normalization based Diffusion GAN—0
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models—0
Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
A multi-artifact EEG denoising by frequency-based deep learning—0
Interacting Diffusion Processes for Event Sequence ForecastingCode0
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
Single channel speech enhancement by colored spectrograms—0
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection—0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model—0
RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery—0
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion ModelsCode0
Fine tuning Pre trained Models for Robustness Under Noisy Labels—0
Complex Image Generation SwinTransformer Network for Audio DenoisingCode0
Improving Diffusion Models for ECG Imputation with an Augmented Template Prior—0
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models—0
Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy—0
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction—0
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—0
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion—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