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 6176–6200 of 7282 papers

TitleStatusHype
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach—0
Backpropagation-Friendly EigendecompositionCode1
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Denoising of MR images with Rician noise using a wider neural network and noise range divisionCode0
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted ImageCode1
NLH: A Blind Pixel-level Non-local Method for Real-world Image DenoisingCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
4D X-Ray CT Reconstruction using Multi-Slice Fusion—0
Deep neural network for fringe pattern filtering and normalisation—0
Robust and interpretable blind image denoising via bias-free convolutional neural networksCode0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space—0
Localized Fourier Analysis for Graph Signal ProcessingCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Differentiable Surface Splatting for Point-based Geometry ProcessingCode0
A Bayesian Model of Dose-Response for Cancer Drug StudiesCode1
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection MethodCode0
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples—0
Convolutional Bipartite Attractor Networks—0
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum—0
Data-driven Thresholding in Denoising with Spectral Graph Wavelet TransformCode0
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersCode0
DVDnet: A Fast Network for Deep Video DenoisingCode0
Learning Deep Image Priors for Blind 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