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 6351–6375 of 7282 papers

TitleStatusHype
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling—0
Robustness Of Saak Transform Against Adversarial Attacks—0
Deep CSI Learning for Gait Biometric Sensing and RecognitionCode0
Multi-Kernel Prediction Networks for Denoising of Burst ImagesCode0
Implicit 3D Orientation Learning for 6D Object Detection from RGB ImagesCode0
New Risk Bounds for 2D Total Variation Denoising—0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
High-Quality Self-Supervised Deep Image DenoisingCode0
Decomposition of Higher-Order Spectra for Blind Multiple-Input Deconvolution, Pattern Identification and SeparationCode0
Learning to Clean: A GAN Perspective—0
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and SignalsCode0
End-to-End Multi-Task Denoising for joint SDR and PESQ OptimizationCode0
Weighted-Sampling Audio Adversarial Example Attack—0
On the Transformation of Latent Space in Autoencoders—0
Learning to compress and search visual data in large-scale systemsCode0
Interpolation and Denoising of Seismic Data using Convolutional Neural Networks—0
An information theoretic approach to the autoencoder—0
A Fourier Disparity Layer representation for Light Fields—0
Comparative Performance Analysis of Image De-noising Techniques—0
Good Similar Patches for Image Denoising—0
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration—0
Quadratic Autoencoder (Q-AE) for Low-dose CT DenoisingCode0
Multi-band Weighted l_p Norm Minimization for Image Denoising—0
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder—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