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 4976–5000 of 7282 papers

TitleStatusHype
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network—0
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring—0
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging—0
Wiener filters on graphs and distributed polynomial approximation algorithms—0
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images—0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented DialogCode0
Assistive Recipe Editing through Critiquing—0
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style TransferCode0
Joint Image Compression and Denoising via Latent-Space Scalability—0
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection OperatorsCode0
Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN—0
PARADISE”:" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining—0
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture—0
Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation—0
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent—0
RoBLEURT Submission for the WMT2021 Metrics Task—0
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image DenoisingCode0
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising—0
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?—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