Multiple Testing Approaches for Removing Background Noise from Images
type: Item
instance of: scholarly article
title: Multiple Testing Approaches for Removing Background Noise from Images
zbMATH Open document ID: 1332.62372
DOI: 10.1007/978-1-4939-0569-0_10
author: John Thomas White
author: Subhashis Ghosal
published in: Springer Proceedings in Mathematics & Statistics
publication date: 2016-02-25T00:00:00Z
Mathematics Subject Classification ID: 62M40
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 6545664
zbMATH Keywords: image denoising
zbMATH Keywords: background noise
zbMATH Keywords: multiple testing
zbMATH Keywords: multiscale representation
zbMATH Keywords: Bayesian approach
zbMATH Keywords: Gaussian mixture model
MaRDI profile type: Publication
full work available at URL: 978-1-4939-0569-0 10
OpenAlex ID: W132840168
cites work: Q4322338
cites work: Predicting False Discovery Proportion Under Dependence
cites work: Bayesian Multiscale Models for Poisson Processes
cites work: Multiscale likelihood analysis and complexity penalized estimation.
cites work: A statistical multiscale framework for Poisson inverse problems
cites work: Exchangeable and partially exchangeable random partitions
cites work: Bayesian Smoothing of Photon-Limited Images with Applications in Astronomy
description: scientific article