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
Probabilistic Noise2Void: Unsupervised Content-Aware DenoisingCode1
Massive Styles Transfer with Limited Labeled DataCode0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Semi-Supervised Teacher-Student Architecture for Relation Extraction—0
Neural Text Style Transfer via Denoising and Reranking—0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
Natural Image Noise DatasetCode0
Convolutional Neural Networks Can Be Deceived by Visual IllusionsCode0
PMS-Net: Robust Haze Removal Based on Patch Map for Single ImagesCode0
Spatially Variant Linear Representation Models for Joint Filtering—0
FOCNet: A Fractional Optimal Control Network for Image DenoisingCode0
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction—0
Solving RED with Weighted Proximal MethodsCode0
Image Denoising with Graph-Convolutional Neural NetworksCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
Vector-Valued Graph Trend Filtering with Non-Convex PenaltiesCode0
Invertible generative models for inverse problems: mitigating representation error and dataset biasCode0
Quantization-Based Regularization for AutoencodersCode0
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise ModelingCode1
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
A Research and Strategy of Remote Sensing Image Denoising Algorithms—0
Tractable Approach to MmWaves Cellular Analysis with FSO Backhauling under Feedback Delay and Hardware LimitationsCode0
Generative Imaging and Image Processing via Generative Encoder—0
Image Fusion via Sparse Regularization with Non-Convex PenaltiesCode0
Segmentation-Aware Image Denoising without Knowing True SegmentationCode0
Underwater Color Restoration Using U-Net Denoising Autoencoder—0
Side Window FilteringCode0
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections—0
Plug-and-Play Methods Provably Converge with Properly Trained DenoisersCode0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
Block Coordinate Regularization by DenoisingCode0
Deep Plug-and-play Prior for Low-rank Tensor Completion—0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Missing Data Imputation with Adversarially-trained Graph Convolutional NetworksCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Multi-level Encoder-Decoder Architectures for Image Restoration—0
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior—0
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN—0
Better Generalization with On-the-fly Dataset Denoising—0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
An approach to image denoising using manifold approximation without clean images—0
Deep Iterative Reconstruction for Phase Retrieval—0
ViDeNN: Deep Blind Video DenoisingCode0
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis—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