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 6226–6250 of 7282 papers

TitleStatusHype
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments—0
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model—0
A Multi-scale Video Denoising Algorithm for Raw Image—0
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications—0
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting—0
Denoising Diffusion Probabilistic Models in Six Simple Steps—0
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network—0
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator—0
Denoising Diffusion Samplers—0
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling—0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Speech Enhancement with Multi-granularity Vector Quantization—0
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity—0
Denoising Diffusion via Image-Based Rendering—0
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training—0
SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms—0
Denoising ECG by Adaptive Filter with Empirical Mode Decomposition—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