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 2826–2850 of 7282 papers

TitleStatusHype
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach—0
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint—0
Correction by Projection: Denoising Images with Generative Adversarial Networks—0
A Simple Analysis of Discretization Error in Diffusion Models—0
Explicit Diffusion of Gaussian Mixture Model Based Image Priors—0
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis—0
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout—0
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data—0
Faster gradient descent and the efficient recovery of images—0
Fast graph-based denoising for point cloud color information—0
FAST-GSC: Fast and Adaptive Semantic Transmission for Generative Semantic Communication—0
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation—0
ASIC Implementation of Denoising Filters for Pacemakers—0
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network—0
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes—0
A First Derivative Potts Model for Segmentation and Denoising Using ILP—0
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback—0
Explainable Deep Learning Framework for SERS Bio-quantification—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication—0
Example-based super-resolution for point-cloud video—0
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation—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