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 6376–6400 of 7282 papers

TitleStatusHype
Basis Pursuit Denoise with Nonsmooth Constraints—0
Image Reconstruction with Predictive Filter FlowCode0
Unprocessing Images for Learned Raw DenoisingCode1
Dense xUnit NetworksCode0
Noise2Void - Learning Denoising from Single Noisy ImagesCode1
Convolutional Neural Networks Deceived by Visual Illusions—0
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds—0
LSD_2 -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition—0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Matrix Product Operator Restricted Boltzmann Machines—0
Towards Adversarial Denoising of Radar Micro-Doppler Signatures—0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?Code0
Towards Unsupervised Speech-to-Text Translation—0
Dynamic Feature Acquisition Using Denoising AutoencodersCode0
Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning—0
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning—0
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Inception-Residual Block based Neural Network for Thermal Image Denoising—0
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data—0
Audio Source Separation Using Variational Autoencoders and Weak Class SupervisionCode0
Image Restoration using Total Variation Regularized Deep Image Prior—0
Neural Nearest Neighbors NetworksCode0
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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