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 5476–5500 of 7282 papers

TitleStatusHype
Bayesian Conditional GAN for MRI Brain Image Synthesis—0
Bayesian Cramér-Rao Bound Estimation with Score-Based Models—0
Bayesian ECG reconstruction using denoising diffusion generative models—0
Bayesian ensemble learning for image denoising—0
Bayesian Formulations for Graph Spectral Denoising—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Bayesian Inference for Neighborhood Filters With Application in Denoising—0
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI—0
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising—0
X-ray ghost tomography: denoising, dose fractionation and mask considerations—0
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO—0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation—0
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions—0
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range—0
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations—0
Single Cell Training on Architecture Search for Image Denoising—0
Benchmarking Denoising Algorithms with Real Photographs—0
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding—0
Speech Dereverberation with A Reverberation Time Shortening Target—0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—0
Single channel speech enhancement by colored spectrograms—0
Better Generalization with On-the-fly Dataset Denoising—0
Show:102550
← PrevPage 220 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