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 6776–6800 of 7282 papers

TitleStatusHype
A Time-Vertex Signal Processing Framework—0
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree—0
A Faster Patch Ordering Method for Image Denoising—0
Denoising Linear Models with Permuted Data—0
A Dual Sparse Decomposition Method for Image Denoising—0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Infinite Sparse Structured Factor Analysis—0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
3D seismic data denoising using two-dimensional sparse coding scheme—0
On the Relation between Color Image Denoising and Classification—0
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising—0
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition—0
Block-Matching Convolutional Neural Network for Image Denoising—0
Nonsymbolic Text Representation—0
Online and Stable Learning of Analysis Operators—0
Image Restoration using Autoencoding Priors—0
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination—0
Discriminative Transfer Learning for General Image Restoration—0
Robust Kronecker-Decomposable Component Analysis for Low-Rank ModelingCode0
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks—0
Learning to Generate Samples from Noise through Infusion TrainingCode0
End-to-End Learning for Structured Prediction Energy Networks—0
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data—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