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 5551–5575 of 7282 papers

TitleStatusHype
Learning local regularization for variational image restoration—0
SLS (Single _1 Selection): a new greedy algorithm with an _1-norm selection rule—0
Using Deep LSD to build operators in GANs latent space with meaning in real spaceCode0
Mask-GVAE: Blind Denoising Graphs via PartitionCode0
Robust Principal Component Analysis: A Median of Means Approach—0
Sampling Based Scene-Space Video Processing—0
Comprehensive Study on Denoising of Medical Images Utilizing Neural Network Based Auto-Encoder—0
No-reference denoising of low-dose CT projections—0
Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentationCode0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Deep High-Resolution Network for Low Dose X-ray CT Denoising—0
[Re] Spatial-Adaptive Network for Single Image DenoisingCode0
High Fidelity Speech Regeneration with Application to Speech Enhancement—0
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
Sparsity Based Autoencoders for Denoising Cluttered Radar Signatures—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
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data—0
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach—0
Exploring ensembles and uncertainty minimization in denoising networks—0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
Stochastic Image Denoising by Sampling from the Posterior Distribution—0
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images—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