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 5376–5400 of 7282 papers

TitleStatusHype
Symmetric Wasserstein AutoencodersCode0
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks—0
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising—0
Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model—0
Multi-Contextual Design of Convolutional Neural Network for Steganalysis—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Pseudo 3D Auto-Correlation Network for Real Image Denoising—0
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise—0
Polarimetric Normal Stereo—0
Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Patch-Based Image Restoration using Expectation Propagation—0
Empirical robustification of pre-trained classifiers—0
Controllable Confidence-Based Image Denoising—0
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic ModelCode0
Removal of speckle noises from ultrasound images using five different deep learning networksCode0
Audio Attacks and Defenses against AED Systems -- A Practical Study—0
Signal processing on simplicial complexes—0
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior—0
Learning the optimal Tikhonov regularizer for inverse problemsCode0
Posterior Temperature Optimization in Variational Inference for Inverse Problems—0
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement—0
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study—0
Phase retrieval with physics informed zero-shot learning—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