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 2451–2475 of 7282 papers

TitleStatusHype
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model—0
Stochastic Localization via Iterative Posterior SamplingCode1
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion ModelCode1
DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image PersonalizationCode2
Classification Diffusion Models: Revitalizing Density Ratio Estimation—0
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image DestripingCode0
Patch-based adaptive temporal filter and residual evaluation—0
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement—0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Target Score Matching—0
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy BiasesCode1
Color Image Denoising Using The Green Channel PriorCode1
PFCM: Poisson flow consistency models for low-dose CT image denoising—0
Rolling Diffusion Models—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Computationally efficient reductions between some statistical models—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
An attempt to generate new bridge types from latent space of denoising diffusion Implicit modelCode0
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Particle Denoising Diffusion SamplerCode1
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Social Physics Informed Diffusion Model for Crowd SimulationCode1
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—0
Show:102550
← PrevPage 99 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