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 5351–5375 of 7282 papers

TitleStatusHype
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
Windowed total variation denoising and noise variance monitoring—0
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting—0
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term—0
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data—0
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video DenoisingCode1
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst ImagesCode1
Exploring ensembles and uncertainty minimization in denoising networks—0
Stochastic Image Denoising by Sampling from the Posterior Distribution—0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Noise Learning Based Denoising Autoencoder—0
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images—0
Image Denoising using Attention-Residual Convolutional Neural Networks—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
Deep Universal Blind Image DenoisingCode1
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Fitting very flexible models: Linear regression with large numbers of parameters—0
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond—0
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network—0
Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation—0
Plug-and-Play Algorithms for Video Snapshot Compressive ImagingCode1
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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