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 5726–5750 of 7282 papers

TitleStatusHype
Blind Image Super-Resolution with Spatial Context Hallucination—0
Multidimensional TV-Stokes for image processing—0
Alternating minimization for a single step TV-Stokes model for image denoising—0
Iterative regularization algorithms for image denoising with the TV-Stokes model—0
A Generative Adversarial Approach with Residual Learning for Dust and Scratches Artifacts Removal—0
Independent finite approximations for Bayesian nonparametric inference—0
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
Deep Autoencoders: From Understanding to Generalization Guarantees—0
Inferring, Predicting, and Denoising Causal Wave Dynamics—0
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm—0
Robust Deep Learning Ensemble against Deception—0
Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning—0
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors—0
Error analysis for denoising smooth modulo signals on a graph—0
Denoising modulo samples: k-NN regression and tightness of SDP relaxationCode0
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models—0
Blind Image Restoration with Flow Based Priors—0
Enhancing and Learning Denoiser without Clean Reference—0
Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning MachineCode0
A Residual Solver and Its Unfolding Neural Network for Total Variation Regularized Models—0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—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