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 4926–4950 of 7282 papers

TitleStatusHype
Game Theory for Adversarial Attacks and DefensesCode0
Score-based diffusion models for accelerated MRICode1
Graphs as Tools to Improve Deep Learning Methods—0
On audio enhancement via online non-negative matrix factorizationCode1
Burst Image Restoration and EnhancementCode1
Generative Modeling with Optimal Transport MapsCode1
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement—0
Accelerated First Order Methods for Variational ImagingCode0
Detection of blue whale vocalisations using a temporal-domain convolutional neural network—0
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation—0
Adaptive Unfolding Total Variation Network for Low-Light Image EnhancementCode1
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration—0
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
Robust Learning with Adaptive Sample Credibility Modeling—0
Learning From Unpaired Data: A Variational Bayes Approach—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
A General Unified Graph Neural Network Framework Against Adversarial Attacks—0
Generative Pseudo-Inverse Memory—0
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level—0
Noise Reconstruction and Removal Network: A New Way to Denoise FIB-SEM Images—0
Automated Channel Pruning with Learned Importance—0
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
Optimizing Few-Step Diffusion Samplers by Gradient Descent—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