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 6401–6425 of 7282 papers

TitleStatusHype
Enhanced CNN for image denoising—0
Deep learning for denoisingCode0
Texture variation adaptive image denoising with nonlocal PCA—0
Convolutional Deblurring for Natural ImagingCode0
Patch-based Interferometric Phase Estimation via Mixture of Gaussian Density Modelling & Non-local Averaging in the Complex Domain—0
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement—0
Investigating the effect of residual and highway connections in speech enhancement modelsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Heteroskedastic PCA: Algorithm, Optimality, and ApplicationsCode0
Learning Multi-Layer Transform Models—0
Unsupervised Neural Text SimplificationCode0
The Wasserstein transform—0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Deep Learning-Based Channel EstimationCode0
No-reference Image Denoising Quality Assessment—0
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy DataCode0
Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform—0
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes—0
Deep Learning for Image Denoising: A Survey—0
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Deep learning cardiac motion analysis for human survival predictionCode0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
Deep Decoder: Concise Image Representations from Untrained Non-convolutional NetworksCode0
The LMU Munich Unsupervised Machine Translation Systems—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