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 6276–6300 of 7282 papers

TitleStatusHype
Better Generalization with On-the-fly Dataset Denoising—0
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN—0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
An approach to image denoising using manifold approximation without clean images—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Deep Iterative Reconstruction for Phase Retrieval—0
ViDeNN: Deep Blind Video DenoisingCode0
Path-Restore: Learning Network Path Selection for Image RestorationCode0
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis—0
An image structure model for exact edge detection—0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions—0
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks—0
Efficient Blind Deblurring under High Noise LevelsCode0
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation—0
One-dimensional Deep Image Prior for Time Series Inverse Problems—0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
Co-Separating Sounds of Visual ObjectsCode0
Total Denoising: Unsupervised Learning of 3D Point Cloud CleaningCode0
A Bayesian Perspective on the Deep Image PriorCode0
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps—0
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
Real Image Denoising with Feature AttentionCode0
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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