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 5001–5025 of 7282 papers

TitleStatusHype
Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss functionCode1
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data—0
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text SummarizationCode1
Bilateral Denoising Diffusion Models—0
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT DenoisingCode1
SwinIR: Image Restoration Using Swin TransformerCode3
Noise2Fast: Fast Self-Supervised Single Image Blind DenoisingCode1
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
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Thermal Image Processing via Physics-Inspired Deep NetworksCode1
Denoising ECG by Adaptive Filter with Empirical Mode Decomposition—0
spectrai: A deep learning framework for spectral dataCode1
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
ILVR: Conditioning Method for Denoising Diffusion Probabilistic ModelsCode1
Physics-based Noise Modeling for Extreme Low-light PhotographyCode1
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Optimal Transport for Unsupervised Denoising LearningCode1
An Operator-Splitting Method for the Gaussian Curvature Regularization Model with Applications to Surface Smoothing and Imaging—0
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence PretrainingCode0
Toward Spatially Unbiased Generative ModelsCode1
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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