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 3901–3925 of 7282 papers

TitleStatusHype
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired DataCode0
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
Information-Theoretic DiffusionCode1
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk ControlCode1
HumanMAC: Masked Motion Completion for Human Motion PredictionCode2
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
Noise-cleaning the precision matrix of fMRI time series—0
DDM^2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion ModelsCode1
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by EnhancingCode1
Generative Diffusion Models on Graphs: Methods and ApplicationsCode2
Using Intermediate Forward Iterates for Intermediate Generator Optimization—0
Diffusion Model for Generative Image Denoising—0
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories—0
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
Learning the Night Sky with Deep Generative Priors—0
Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising—0
Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update—0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update—0
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models—0
Stable Target Field for Reduced Variance Score Estimation in Diffusion ModelsCode1
Optimizing DDPM Sampling with Shortcut Fine-TuningCode1
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion ModelsCode1
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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