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
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
Raw Waveform-based Speech Enhancement by Fully Convolutional Networks—0
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System—0
Denoising Adversarial AutoencodersCode1
Group Sparsity Residual Constraint for Image Denoising—0
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation—0
Variational Inference using Implicit Distributions—0
Image Analysis Using a Dual-Tree M-Band Wavelet Transform—0
Learning Non-local Image Diffusion for Image Denoising—0
CT Image Denoising with Perceptive Deep Neural Networks—0
Synthesis versus analysis in patch-based image priors—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