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 6326–6350 of 7282 papers

TitleStatusHype
Plug and play methods for magnetic resonance imaging (long version)—0
Proximal Splitting Networks for Image Restoration—0
Improved Self-Supervised Deep Image Denoising—0
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing—0
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction—0
Neural Empirical Bayes—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Unpaired image denoising using a generative adversarial network in X-ray CT—0
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation—0
Quaternion Convolutional Neural NetworksCode0
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled DataCode0
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders—0
Can learning from natural image denoising be used for seismic data interpolation?Code0
SURE-fuse WFF: A Multi-resolution Windowed Fourier Analysis for Interferometric Phase Denoising—0
Event-driven Video Frame SynthesisCode0
Matrix denoising for weighted loss functions and heterogeneous signals—0
Deep Learning for Low-Dose CT Denoising—0
Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based MethodCode0
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty—0
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation—0
Point cloud denoising based on tensor Tucker decomposition—0
Convolutional Dictionary Regularizers for Tomographic Inversion—0
Advanced Denoising for X-ray Ptychography—0
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing—0
Color Image and Multispectral Image Denoising Using Block Diagonal Representation—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