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 6826–6850 of 7282 papers

TitleStatusHype
Sparse Factorization Layers for Neural Networks with Limited Supervision—0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
An Empirical Study of ADMM for Nonconvex Problems—0
Generalized Deep Image to Image RegressionCode0
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence—0
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis—0
Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering—0
On-Demand Learning for Deep Image RestorationCode1
Joint Visual Denoising and Classification using Deep LearningCode0
A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel—0
Split LBI: An Iterative Regularization Path with Structural Sparsity—0
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data—0
Proximal Deep Structured Models—0
Wasserstein Training of Restricted Boltzmann Machines—0
Select-and-Sample for Spike-and-Slab Sparse Coding—0
Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.—0
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter—0
Graph-Based Manifold Frequency Analysis for Denoising—0
Dictionary Learning with Equiprobable Matching Pursuit—0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Times series averaging and denoising from a probabilistic perspective on time-elastic kernelsCode0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation—0
Image Segmentation Using Overlapping Group Sparsity—0
Straight to Shapes: Real-time Detection of Encoded ShapesCode0
Learning to Distill: The Essence Vector Modeling Framework—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