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 7201–7225 of 7282 papers

TitleStatusHype
k-Sparse AutoencodersCode0
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors—0
Adaptive dropout for training deep neural networks—0
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising—0
Reconciling "priors" & "priors" without prejudice?—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Memoized Online Variational Inference for Dirichlet Process Mixture Models—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Estimating LASSO Risk and Noise Level—0
On a non-local spectrogram for denoising one-dimensional signals—0
Neighborhood filters and the decreasing rearrangement—0
Inference of Network Summary Statistics Through Network Denoising—0
Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing—0
A Non-Local Means Filter for Removing the Poisson Noise—0
Sparsity Based Poisson Denoising with Dictionary Learning—0
On Convergent Finite Difference Schemes for Variational - PDE Based Image Processing—0
Temporal Autoencoding Improves Generative Models of Time Series—0
Single image super resolution in spatial and wavelet domain—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization—0
A Unified Framework for Multi-Sensor HDR Video Reconstruction—0
Faster gradient descent and the efficient recovery of images—0
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes—0
Bayesian ensemble learning for image denoising—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