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 3376–3400 of 7282 papers

TitleStatusHype
Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data—0
supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models—0
Fast LiDAR Upsampling using Conditional Diffusion ModelsCode0
Dual-domain Collaborative Denoising for Social Recommendation—0
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models—0
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
Invariant Risk Minimization Is A Total Variation ModelCode0
Investigating Self-Supervised Image Denoising with Denaturation—0
Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data—0
Streamlining Image Editing with Layered Diffusion Brushes—0
Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Noise propagation and MP-PCA image denoising for high-resolution quantitative T2* and magnetic susceptibility mapping (QSM)—0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models—0
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image 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