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 2751–2775 of 7282 papers

TitleStatusHype
On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance—0
Entropy stable conservative flux form neural networks—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—0
Denoising Fisher Training For Neural Implicit Samplers—0
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity—0
A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR—0
Adaptive Domain Learning for Cross-domain Image Denoising—0
HC^3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis—0
Statistical guarantees for denoising reflected diffusion models—0
Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential EquationsCode0
Supervised Score-Based Modeling by Gradient Boosting—0
Diffusion Models as Cartoonists! The Curious Case of High Density Regions—0
TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images—0
Scalable AI Framework for Defect Detection in Metal Additive Manufacturing—0
pcaGAN: Improving Posterior-Sampling cGANs via Principal Component RegularizationCode0
Constrained Diffusion Implicit Models—0
An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation—0
Denoising Diffusion Models for Anomaly Localization in Medical Images—0
There and Back Again: On the relation between noises, images, and their inversions in diffusion modelsCode0
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data—0
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative ModelsCode0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
DiffPAD: Denoising Diffusion-based Adversarial Patch DecontaminationCode0
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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