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 6551–6575 of 7282 papers

TitleStatusHype
Diffusion Models: Tutorial and Survey—0
Diffusion Models Using a Single Equation—0
Diffusion Models Without Attention—0
Improving Probabilistic Diffusion Models With Optimal Covariance Matching—0
Diffusion Model with Perceptual Loss—0
Diffusion Model with Representation Alignment for Protein Inverse Folding—0
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation—0
Stochastic Image Denoising by Sampling from the Posterior Distribution—0
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition—0
3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising—0
Video-T1: Test-Time Scaling for Video Generation—0
Stochastic Orthogonal Regularization for deep projective priors—0
Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing—0
A lightweight convolutional neural network for image denoising with fine details preservation capability—0
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT—0
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising—0
Diffusion Priors In Variational Autoencoders—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
Diffusion Probabilistic Fields—0
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI—0
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation—0
A Light Label Denoising Method with the Internal Data Guidance—0
Diffusion Probabilistic Models for Compressive SAR Imaging—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