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 6626–6650 of 7282 papers

TitleStatusHype
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural networkCode0
Denoising of image gradients and total generalized variation denoising—0
Deep Burst DenoisingCode0
Learning to Navigate by Growing Deep Networks—0
Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks—0
Chaining Identity Mapping Modules for Image Denoising—0
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition—0
Sparse learning of stochastic dynamic equationsCode0
On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising—0
Burst Denoising with Kernel Prediction NetworksCode0
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification—0
OnACID: Online Analysis of Calcium Imaging Data in Real Time—0
RF-Based Direction Finding of UAVs Using DNNCode0
InverseNet: Solving Inverse Problems with Splitting Networks—0
Properties on n-dimensional convolution for image deconvolution—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Deep Image PriorCode1
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders—0
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders—0
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery—0
Video Enhancement with Task-Oriented FlowCode1
SPINE: SParse Interpretable Neural EmbeddingsCode0
A Face Fairness Framework for 3D Meshes—0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising—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