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 6701–6750 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
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization—0
Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data—0
Anomaly Detection with Robust Deep AutoencodersCode0
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing—0
An End-to-End Compression Framework Based on Convolutional Neural NetworksCode0
From Patches to Images: A Nonparametric Generative ModelCode0
Image Denoising via CNNs: An Adversarial Approach—0
Dictionary Learning Based on Sparse Distribution Tomography—0
Transfer Learning with Label Noise—0
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual NetworkCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis—0
Kernel-predicting convolutional networks for denoising monte carlo renderings.—0
Wide Inference Network for Image Denoising via Learning Pixel-distribution PriorCode0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
Impulsive noise removal from color images with morphological filtering—0
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing—0
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising—0
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising—0
Benchmarking Denoising Algorithms with Real Photographs—0
Convolutional Dictionary Learning: Acceleration and ConvergenceCode0
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising—0
Removing Rain From Single Images via a Deep Detail Network—0
Additive Component Analysis—0
Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm—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