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 6701–6725 of 7282 papers

TitleStatusHype
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video—0
Robust non-local means filter for ultrasound image denoising—0
A First Derivative Potts Model for Segmentation and Denoising Using ILP—0
Contrastive Principal Component AnalysisCode0
Learning quadrangulated patches for 3D shape parameterization and completion—0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
Neural Affine Grayscale Image Denoising—0
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix FactorizationCode0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Informed Non-convex Robust Principal Component Analysis with Features—0
A Denoising Loss Bound for Neural Network based Universal Discrete Denoisers—0
Une véritable approche _0 pour l'apprentissage de dictionnaire—0
Deep Mean-Shift Priors for Image RestorationCode0
Calibration of depth cameras using denoised depth images—0
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images—0
On the exact relationship between the denoising function and the data distribution—0
An inner-loop free solution to inverse problems using deep neural networks—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Convolutional Sparse Coding with Overlapping Group Norms—0
On denoising autoencoders trained to minimise binary cross-entropy—0
Sharpness-aware Low dose CT denoising using conditional generative adversarial networkCode0
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Dilated Deep Residual Network for Image Denoising—0
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders—0
An ELU Network with Total Variation for Image Denoising—0
Show:102550
← PrevPage 269 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