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 2951–2975 of 7282 papers

TitleStatusHype
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture—0
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy—0
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures—0
Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images—0
An Attention Free Conditional Autoencoder For Anomaly Detection in Cryptocurrencies—0
Addressing Negative Transfer in Diffusion Models—0
Accelerating Video Diffusion Models via Distribution Matching—0
Design Editing for Offline Model-based Optimization—0
Design a Metric Robust to Complicated High Dimensional Noise for Efficient Manifold Denoising—0
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images—0
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—0
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics—0
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Depth image denoising using nuclear norm and learning graph model—0
Depth-guided NeRF Training via Earth Mover's Distance—0
Density-based Denoising of Point Cloud—0
Density—0
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog—0
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising—0
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement—0
Dense residual Transformer for image denoising—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—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