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 6201–6250 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
Localized Fourier Analysis for Graph Signal ProcessingCode0
Differentiable Surface Splatting for Point-based Geometry ProcessingCode0
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection MethodCode0
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples—0
Convolutional Bipartite Attractor Networks—0
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum—0
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersCode0
Data-driven Thresholding in Denoising with Spectral Graph Wavelet TransformCode0
Learning Deep Image Priors for Blind Image Denoising—0
DVDnet: A Fast Network for Deep Video DenoisingCode0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Massive Styles Transfer with Limited Labeled DataCode0
Convolutional Neural Networks Can Be Deceived by Visual IllusionsCode0
Spatially Variant Linear Representation Models for Joint Filtering—0
Semi-Supervised Teacher-Student Architecture for Relation Extraction—0
FOCNet: A Fractional Optimal Control Network for Image DenoisingCode0
Natural Image Noise DatasetCode0
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning—0
Neural Text Style Transfer via Denoising and Reranking—0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
PMS-Net: Robust Haze Removal Based on Patch Map for Single ImagesCode0
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Solving RED with Weighted Proximal MethodsCode0
Vector-Valued Graph Trend Filtering with Non-Convex PenaltiesCode0
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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