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 6276–6300 of 7282 papers

TitleStatusHype
When AWGN-based Denoiser Meets Real NoisesCode0
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels—0
Speech denoising by parametric resynthesis—0
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks—0
A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming—0
Non-linear aggregation of filters to improve image denoisingCode0
Deep Network for Capacitive ECG Denoising—0
Regularizing Trajectory Optimization with Denoising Autoencoders—0
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders—0
Increasing Iterate Averaging for Solving Saddle-Point Problems—0
Learning Quadrangulated Patches For 3D Shape Processing—0
DeepRED: Deep Image Prior Powered by REDCode0
Residual Non-local Attention Networks for Image RestorationCode0
Semantic denoising autoencoders for retinal optical coherence tomography—0
A lightweight convolutional neural network for image denoising with fine details preservation capability—0
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders—0
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor—0
Plug and play methods for magnetic resonance imaging (long version)—0
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations—0
Proximal Splitting Networks for Image Restoration—0
Improved Self-Supervised Deep Image Denoising—0
Low-rankness of Complex-valued Spectrogram and Its Application to Phase-aware Audio Processing—0
Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction—0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Neural Empirical Bayes—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