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 6176–6200 of 7282 papers

TitleStatusHype
Semi-supervised representation learning via dual autoencoders for domain adaptationCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation DatasetCode0
Pyramid Real Image Denoising NetworkCode0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
Few-Shot Meta-Denoising—0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network—0
Summary Refinement through DenoisingCode0
Feature Graph Learning for 3D Point Cloud Denoising—0
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders—0
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration—0
Deep Graph-Convolutional Image DenoisingCode0
Discrete Object Generation with Reversible Inductive ConstructionCode0
Low-rank matrix completion and denoising under Poisson noise—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders—0
A Projectional Ansatz to ReconstructionCode0
Convolutional dictionary learning based auto-encoders for natural exponential-family distributionsCode0
Fast and Provable ADMM for Learning with Generative Priors—0
Multi-level Wavelet Convolutional Neural NetworksCode0
Regularizing linear inverse problems with convolutional neural networks—0
Blind Universal Bayesian Image Denoising with Gaussian Noise Level LearningCode0
Feature-Based Image Clustering and Segmentation Using WaveletsCode0
Brno Mobile OCR DatasetCode0
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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