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 6376–6400 of 7282 papers

TitleStatusHype
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—0
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising—0
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration—0
DiffBrush:Just Painting the Art by Your Hands—0
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models—0
Diff-CAPTCHA: An Image-based CAPTCHA with Security Enhanced by Denoising Diffusion Model—0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow—0
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models—0
DiffDesign: Controllable Diffusion with Meta Prior for Efficient Interior Design Generation—0
A multi-artifact EEG denoising by frequency-based deep learning—0
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization—0
Differentiable Forward and Backward Fixed-Point Iteration Layers—0
Stable Diffusion with Continuous-time Neural Network—0
Differentiable Programming for Piecewise Polynomial Functions—0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation—0
StableGarment: Garment-Centric Generation via Stable Diffusion—0
DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks—0
MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets—0
DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization—0
Show:102550
← PrevPage 256 of 292Next →
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