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 7126–7150 of 7282 papers

TitleStatusHype
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
From neural PCA to deep unsupervised learningCode0
An algorithm for improving Non-Local Means operators via low-rank approximation—0
Modular proximal optimization for multidimensional total-variation regularizationCode0
Robust Piecewise-Constant Smoothing: M-Smoother Revisited—0
Directional Bilateral Filters—0
A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes—0
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging—0
Feature Learning from Incomplete EEG with Denoising Autoencoder—0
Generalized Low Rank ModelsCode1
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis—0
RENOIR - A Dataset for Real Low-Light Image Noise ReductionCode0
Domain Adaptive Neural Networks for Object Recognition—0
Statistical Estimation: From Denoising to Sparse Regression and Hidden Cliques—0
Image Denoising using New Adaptive Based Median Filters—0
_1-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update—0
A General Framework for Fast Stagewise Algorithms—0
A fast patch-dictionary method for whole image recovery—0
Gradient Distribution Priors for Biomedical Image Processing—0
Tree-based iterated local search for Markov random fields with applications in image analysis—0
Image Inpainting Using Directional Tensor Product Complex Tight Framelets—0
Optimally Stabilized PET Image Denoising Using Trilateral Filtering—0
The jump set under geometric regularisation. Part 2: Higher-order approaches—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