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 2076–2100 of 7282 papers

TitleStatusHype
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems—0
Zero-Shot Low-dose CT Denoising via Sinogram Flicking—0
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy—0
Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression—0
Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledgeCode0
Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction—0
Releasing Differentially Private Event Logs Using Generative ModelsCode0
AstroClearNet: Deep image prior for multi-frame astronomical image restoration—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—0
OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model—0
Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures—0
Improved Stochastic Texture Filtering Through Sample Reuse—0
DDPM Score Matching and Distribution Learning—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning—0
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis—0
Turbocharging Fluid Antenna Multiple Access—0
Variational Self-Supervised Learning—0
Simultaneous Motion And Noise Estimation with Event CamerasCode0
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking—0
On the Connection Between Diffusion Models and Molecular Dynamics—0
Dynamic Importance in Diffusion U-Net for Enhanced Image SynthesisCode0
FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement—0
Roto-Translation Invariant Metrics on Position-Orientation Space—0
DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework—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