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 6676–6700 of 7282 papers

TitleStatusHype
Iterative PET Image Reconstruction Using Convolutional Neural Network RepresentationCode0
Reconstruction of Hidden Representation for Robust Feature Extraction—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
Video Denoising and Enhancement via Dynamic Video Layering—0
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITKCode0
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video DenoisingCode0
Predictor Combination at Test Time—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video DenoisingCode0
A Variational Approach to Shape-from-shading Under Natural Illumination—0
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
Learning quadrangulated patches for 3D shape parameterization and completion—0
Contrastive Principal Component AnalysisCode0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
Neural Affine Grayscale Image Denoising—0
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix FactorizationCode0
Informed Non-convex Robust Principal Component Analysis with Features—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Une véritable approche _0 pour l'apprentissage de dictionnaire—0
Deep Mean-Shift Priors for Image RestorationCode0
A Denoising Loss Bound for Neural Network based Universal Discrete Denoisers—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