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 6501–6550 of 7282 papers

TitleStatusHype
Denoising Distantly Supervised Open-Domain Question AnsweringCode0
Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.Code0
Speech Denoising with Deep Feature LossesCode1
Coupled Dictionary Learning for Multi-contrast MRI ReconstructionCode0
Multi-modal Image Processing based on Coupled Dictionary Learning—0
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT AngiographyCode0
Multimodal Image Denoising based on Coupled Dictionary Learning—0
A deep learning framework for segmentation of retinal layers from OCT images—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
MedGAN: Medical Image Translation using GANs—0
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina—0
Learning Dynamics of Linear Denoising AutoencodersCode0
Identifying Recurring Patterns with Deep Neural Networks for Natural Image DenoisingCode0
Adaptive Denoising of Signals with Local Shift-Invariant Structure—0
Non-Local Recurrent Network for Image RestorationCode0
Regularization by Denoising: Clarifications and New InterpretationsCode0
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)—0
Holographic Neural Architectures—0
Deep End-to-End Time-of-Flight Imaging—0
A High-Quality Denoising Dataset for Smartphone Cameras—0
Multi-Scale Weighted Nuclear Norm Image Restoration—0
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Faces—0
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling—0
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction—0
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift—0
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural NetworkCode0
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework—0
Robustifying Models Against Adversarial Attacks by Langevin Dynamics—0
CocoNet: A deep neural network for mapping pixel coordinates to color valuesCode0
Adversarial Regularizers in Inverse ProblemsCode0
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training—0
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms—0
Unsupervised Learning with Stein's Unbiased Risk EstimatorCode0
Distributed Cartesian Power Graph Segmentation for Graphon Estimation—0
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial IterationsCode0
Rate-Optimal Denoising with Deep Neural Networks—0
State-Denoised Recurrent Neural Networks—0
Deep Energy Estimator NetworksCode1
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration—0
Multi-level Wavelet-CNN for Image RestorationCode0
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal DenoisingCode0
k-Space Deep Learning for Accelerated MRICode1
Network Enhancement: a general method to denoise weighted biological networks—0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Moiré Photo Restoration Using Multiresolution Convolutional Neural NetworksCode0
Acceleration of RED via Vector ExtrapolationCode0
Learning to See in the DarkCode1
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing—0
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT 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