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 5276–5300 of 7282 papers

TitleStatusHype
iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging—0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
A Neural Network for SemigroupsCode0
UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color SpaceCode1
An unsupervised deep learning framework for medical image denoising—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse RepresentationsCode1
Unified Pre-training for Program Understanding and GenerationCode1
Robust graph-filter identification with graph denoising regularizationCode0
COLA-Net: Collaborative Attention Network for Image RestorationCode1
Time-Ordered Recent Event (TORE) Volumes for Event CamerasCode1
Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal FusionCode1
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation—0
Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)Code0
High Perceptual Quality Image Denoising with a Posterior Sampling CGANCode1
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary LearningCode1
Prediction of financial time series using LSTM and data denoising methods—0
BM3D vs 2-Layer ONN—0
Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient MaskCode1
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
Efficient Deep Image Denoising via Class Specific Convolution—0
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding PriorCode0
Deep Learning strategies for ProtoDUNE raw data denoisingCode0
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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