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 6251–6300 of 7282 papers

TitleStatusHype
Path-Restore: Learning Network Path Selection for Image RestorationCode0
An image structure model for exact edge detection—0
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation—0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions—0
Efficient Blind Deblurring under High Noise LevelsCode0
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks—0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
One-dimensional Deep Image Prior for Time Series Inverse Problems—0
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Total Denoising: Unsupervised Learning of 3D Point Cloud CleaningCode0
Co-Separating Sounds of Visual ObjectsCode0
Real Image Denoising with Feature AttentionCode0
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional NetworksCode0
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNetCode0
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps—0
A Bayesian Perspective on the Deep Image PriorCode0
Learning Deformable Kernels for Image and Video DenoisingCode1
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols—0
Patch redundancy in images: a statistical testing framework and some applications—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—0
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images—0
Bilingual-GAN: A Step Towards Parallel Text Generation—0
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation—0
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL—0
When AWGN-based Denoiser Meets Real NoisesCode0
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels—0
Speech denoising by parametric resynthesis—0
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks—0
A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming—0
Non-linear aggregation of filters to improve image denoisingCode0
Deep Network for Capacitive ECG Denoising—0
Regularizing Trajectory Optimization with Denoising Autoencoders—0
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders—0
Increasing Iterate Averaging for Solving Saddle-Point Problems—0
Learning Quadrangulated Patches For 3D Shape Processing—0
DeepRED: Deep Image Prior Powered by REDCode0
Residual Non-local Attention Networks for Image RestorationCode0
Semantic denoising autoencoders for retinal optical coherence tomography—0
A lightweight convolutional neural network for image denoising with fine details preservation capability—0
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders—0
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor—0
Plug and play methods for magnetic resonance imaging (long version)—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
Proximal Splitting Networks for Image Restoration—0
Improved Self-Supervised Deep Image Denoising—0
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing—0
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Neural Empirical Bayes—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