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 5301–5325 of 7282 papers

TitleStatusHype
The potential of self-supervised networks for random noise suppression in seismic data—0
Statistical limits of dictionary learning: random matrix theory and the spectral replica method—0
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval—0
View Blind-spot as Inpainting: Self-Supervised Denoising with Mask Guided Residual Convolution—0
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer—0
Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography imagesCode0
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study—0
Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection—0
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement—0
Automatic Online Multi-Source Domain AdaptationCode0
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding—0
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance—0
Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm—0
Image Denoising Inspired by Quantum Many-Body physics—0
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data—0
Bilateral Denoising Diffusion Models—0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness—0
Electroencephalogram Signal Processing with Independent Component Analysis and Cognitive Stress Classification using Convolutional Neural Networks—0
Sparse-Denoising Methods for Extracting Desaturation Transients in Cerebral Oxygenation Signals of Preterm Infants—0
Denoising ECG by Adaptive Filter with Empirical Mode Decomposition—0
End-to-End Adaptive Monte Carlo Denoising and Super-Resolution—0
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection—0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
Learning Fair Face Representation With Progressive Cross Transformer—0
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence PretrainingCode0
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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