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 6501–6525 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
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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