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 3101–3125 of 7282 papers

TitleStatusHype
Denoising Functional Maps: Diffusion Models for Shape Correspondence—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Denoising Fisher Training For Neural Implicit Samplers—0
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—0
BJTU-WeChat's Systems for the WMT22 Chat Translation Task—0
An algorithm for improving Non-Local Means operators via low-rank approximation—0
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals—0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
Denoising ECG by Adaptive Filter with Empirical Mode Decomposition—0
Bitwise Operations of Cellular Automaton on Gray-scale Images—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training—0
Denoising Diffusion via Image-Based Rendering—0
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity—0
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling—0
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys—0
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution—0
Denoising Diffusion Samplers—0
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator—0
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders—0
Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
Denoising Diffusion Probabilistic Models in Six Simple Steps—0
Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting—0
Show:102550
← PrevPage 125 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