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 6201–6225 of 7282 papers

TitleStatusHype
Seismic data denoising and deblending using deep learning—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation—0
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders—0
Self-supervised Hyperspectral Image Restoration using Separable Image Prior—0
Sparse Solutions of a Class of Constrained Optimization Problems—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow EstimationCode0
Automated characterization of noise distributions in diffusion MRI dataCode0
One Embedding To Do Them All—0
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction—0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach—0
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural NetworksCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
NLH: A Blind Pixel-level Non-local Method for Real-world Image DenoisingCode0
Denoising of MR images with Rician noise using a wider neural network and noise range divisionCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
4D X-Ray CT Reconstruction using Multi-Slice Fusion—0
Deep neural network for fringe pattern filtering and normalisation—0
Robust and interpretable blind image denoising via bias-free convolutional neural networksCode0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space—0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Show:102550
← PrevPage 249 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