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 5051–5075 of 7282 papers

TitleStatusHype
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models—0
Unsupervised Reservoir Computing for Multivariate Denoising of Severely Contaminated Signals—0
Score-based Diffusion Models in Function Space—0
Unsupervised Sketch to Photo Synthesis—0
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions—0
Score-based Generative Models with Adaptive Momentum—0
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction—0
Score-Based Model for Low-Rank Tensor Recovery—0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
Score-based Self-supervised MRI Denoising—0
Score-Based Turbo Message Passing for Plug-and-Play Compressive Image Recovery—0
Score Distillation Sampling with Learned Manifold Corrective—0
Score Distillation via Reparametrized DDIM—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring—0
Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems—0
Score matching for sub-Riemannian bridge sampling—0
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation—0
Score-Optimal Diffusion Schedules—0
Unsupervised Visual Defect Detection with Score-Based Generative Model—0
Untrained Perceptual Loss for image denoising of line-like structures in MR images—0
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization—0
Screen Content Image Segmentation Using Sparse-Smooth Decomposition—0
Show:102550
← PrevPage 203 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