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 626–650 of 7282 papers

TitleStatusHype
3D Priors-Guided Diffusion for Blind Face RestorationCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
An Evaluation of Deep Learning Models for Stock Market Trend PredictionCode1
PFDiff: Training-free Acceleration of Diffusion Models through the Gradient Guidance of Past and FutureCode1
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
Joint Graph Rewiring and Feature Denoising via Spectral ResonanceCode1
Image Denoising Using Green Channel PriorCode1
Efficient Diffusion Transformer with Step-wise Dynamic Attention MediatorsCode1
MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent ImputationCode1
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature ScalingCode1
Pretrained-Guided Conditional Diffusion Models for Microbiome Data AnalysisCode1
Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics DataCode1
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising TrainingCode1
CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical SystemsCode1
Interpreting Low-level Vision Models with Causal Effect MapsCode1
Denoising Lévy Probabilistic ModelsCode1
Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight ImagingCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over TimeCode1
D^4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-OnCode1
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant AttentionCode1
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracyCode1
Chip Placement with Diffusion ModelsCode1
Region Attention Transformer for Medical Image RestorationCode1
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive TrainingCode1
Show:102550
← PrevPage 26 of 292Next →
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