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 1401–1425 of 7282 papers

TitleStatusHype
An Analysis and Implementation of the HDR+ Burst Denoising MethodCode1
MAAD: A Model and Dataset for "Attended Awareness" in DrivingCode1
Toward Degradation-Robust Voice ConversionCode1
Rethinking Noise Synthesis and Modeling in Raw DenoisingCode1
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising LearningCode1
Score-based diffusion models for accelerated MRICode1
Burst Image Restoration and EnhancementCode1
On audio enhancement via online non-negative matrix factorizationCode1
Generative Modeling with Optimal Transport MapsCode1
Adaptive Unfolding Total Variation Network for Low-Light Image EnhancementCode1
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised LearningCode1
BARTpho: Pre-trained Sequence-to-Sequence Models for VietnameseCode1
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-LabelingCode1
FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise RemovalCode1
WINNet: Wavelet-inspired Invertible Network for Image DenoisingCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent VariablesCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
Sentence Bottleneck Autoencoders from Transformer Language ModelsCode1
Rethinking Deep Image Prior for DenoisingCode1
Self-supervised Neural Networks for Spectral Snapshot Compressive ImagingCode1
Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss functionCode1
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text SummarizationCode1
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT DenoisingCode1
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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