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 2476–2500 of 7282 papers

TitleStatusHype
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Time Series Diffusion in the Frequency DomainCode2
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
Blue noise for diffusion modelsCode2
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Context-Aware Automated Passenger Counting Data Denoising—0
Denoising Diffusion Probabilistic Models in Six Simple Steps—0
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal DenoisingCode1
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
Unified Discrete Diffusion for Categorical DataCode1
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models—0
The last Dance : Robust backdoor attack via diffusion models and bayesian approach—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
Denoising Diffusion via Image-Based Rendering—0
DexDiffuser: Generating Dexterous Grasps with Diffusion Models—0
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells—0
A Lennard-Jones Layer for Distribution Normalization—0
ViewFusion: Learning Composable Diffusion Models for Novel View SynthesisCode1
Diffusive Gibbs SamplingCode1
Guidance with Spherical Gaussian Constraint for Conditional DiffusionCode2
Denoising Time Cycle Modeling for Recommendation—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