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

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Showing 4976–5000 of 7282 papers

TitleStatusHype
Improving Reproducibility and Performance of Radiomics in Low Dose CT using Cycle GANs—0
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving—0
The potential of self-supervised networks for random noise suppression in seismic data—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Statistical limits of dictionary learning: random matrix theory and the spectral replica method—0
Dynamic Attentive Graph Learning for Image RestorationCode1
WINNet: Wavelet-inspired Invertible Network for Image DenoisingCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval—0
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent VariablesCode1
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
Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection—0
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study—0
Automatic Online Multi-Source Domain AdaptationCode0
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement—0
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
Sentence Bottleneck Autoencoders from Transformer Language ModelsCode1
Image Denoising Inspired by Quantum Many-Body physics—0
Rethinking Deep Image Prior for DenoisingCode1
Self-supervised Neural Networks for Spectral Snapshot Compressive ImagingCode1
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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