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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6626–6650 of 7282 papers

TitleStatusHype
A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity—0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
Robust Kronecker Component Analysis—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
Denoising Dictionary Learning Against Adversarial Perturbations—0
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures—0
Learning Implicit Brain MRI Manifolds with Deep Learning—0
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging—0
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data—0
Image denoising through bivariate shrinkage function in framelet domain—0
Deep Stacked Networks with Residual Polishing for Image Inpainting—0
Dendritic error backpropagation in deep cortical microcircuitsCode0
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
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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