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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7251–7282 of 7282 papers

TitleStatusHype
Astronomical Image Denoising Using Dictionary Learning—0
Multiscale Hybrid Non-local Means Filtering Using Modified Similarity Measure—0
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals—0
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising—0
On the convergence of the IRLS algorithm in Non-Local Patch Regression—0
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI—0
Convex Variational Image Restoration with Histogram Priors—0
Audio Classical Composer Identification by Deep Neural Network—0
Feature vector regularization in machine learning—0
Kernel Estimation from Salient Structure for Robust Motion Deblurring—0
Hierarchical spike coding of sound—0
Image Denoising and Inpainting with Deep Neural Networks—0
Natural Images, Gaussian Mixtures and Dead Leaves—0
What Regularized Auto-Encoders Learn from the Data Generating Distribution—0
A Truncated EM Approach for Spike-and-Slab Sparse Coding—0
No-Reference Image Quality Assessment in the Spatial DomainCode0
Reduction of Non-stationary Noise for a Robotic Living Assistant using Sparse Non-negative Matrix Factorization—0
Poisson noise reduction with non-local PCA—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
A Denoising View of Matrix Completion—0
Generating more realistic images using gated MRF's—0
A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction—0
Regularized estimation of image statistics by Score Matching—0
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content—0
Nonlinear Filter Based Image Denoising Using AMF Approach—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Fast Image Deconvolution using Hyper-Laplacian Priors—0
An Homotopy Algorithm for the Lasso with Online Observations—0
Natural Image Denoising with Convolutional Networks—0
Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images—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