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 6026–6050 of 7282 papers

TitleStatusHype
Distributed Machine Learning with Sparse Heterogeneous Data—0
Noise2Blur: Online Noise Extraction and Denoising—0
Financial Market Directional Forecasting With Stacked Denoising Autoencoder—0
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models—0
AGEM: Solving Linear Inverse Problems via Deep Priors and SamplingCode0
適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)—0
Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images—0
CURL: Neural Curve Layers for Global Image EnhancementCode0
Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data—0
Leveraging Self-supervised Denoising for Image SegmentationCode0
Fully Unsupervised Probabilistic Noise2VoidCode0
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO—0
Noise Robust Generative Adversarial NetworksCode0
Adaptive Estimation of Multivariate Piecewise Polynomials and Bounded Variation Functions by Optimal Decision Trees—0
Matrix Completion using Kronecker Product Approximation—0
Microscopy Image Restoration with Deep Wiener-Kolmogorov filtersCode0
ColorFool: Semantic Adversarial ColorizationCode0
Discriminative training of conditional random fields with probably submodular constraints—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—0
Fast and Flexible Image Blind Denoising via Competition of Experts—0
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems—0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
MRI denoising using Deep Learning and Non-local averaging—0
LIDIA: Lightweight Learned Image Denoising with Instance AdaptationCode0
Towards the Automation of Deep Image Prior—0
Show:102550
← PrevPage 242 of 292Next →
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