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 3801–3825 of 7282 papers

TitleStatusHype
Feature-guided score diffusion for sampling conditional densities—0
Feature Learning from Incomplete EEG with Denoising Autoencoder—0
Feature Losses for Adversarial Robustness—0
Feature Prediction Diffusion Model for Video Anomaly Detection—0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
Feature vector regularization in machine learning—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL—0
Federated Learning for Diffusion Models—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions—0
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising—0
FeedEdit: Text-Based Image Editing with Dynamic Feedback Regulation—0
FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments—0
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals—0
Few-Shot Concept Unlearning with Low Rank Adaptation—0
Few-Shot Meta-Denoising—0
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks—0
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism—0
Filter characteristics in image decomposition with singular spectrum analysis—0
Filtered Iterative Denoising for Linear Inverse Problems—0
Filter Forests for Learning Data-Dependent Convolutional Kernels—0
Financial Market Directional Forecasting With Stacked Denoising Autoencoder—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