Journal of Scientific Instruments Editor: Dr H R Lang Vol 28 and Supplement No 1, 1951 Pp xvi + 388 + iii + 80 (London: Institute of Physics, 1951) Bound, £3 12s; unbound, £3

/pdf/a-review-of-scientific-instruments-50cfd4j98s.pdf

A Review of Scientific Instruments

This paper deals with noise parameter estimation. We assume observations corrupted by noise modelled as a sum of two random processes: one Poisson and the other a (nonzero mean) Gaussian. Such problems arise in various applications, e.g. in astronomy and confocal microscopy imaging. To estimate noise parameters, we propose an iterative algorithm based on an Expectation-Maximization approach. This allows us to jointly estimate the scale parameter of the Poisson component and the mean and variance of the Gaussian one. Moreover, an adequate initialization based on cumulants is provided. Numerical difficulties arising from the procedure are also addressed. To validate the proposed method in terms of accuracy and robustness, tests are performed on synthetic data. The good performance of the method is also demonstrated in a denoising experiment on real data.

https://hal-upec-upem.archives-ouvertes.fr/hal-00733633/document

An EM approach for Poisson-Gaussian noise modeling

The problem of estimating the parameters of a Poisson-Gaussian model from experimental data has recently raised much interest in various applications, for instance in confocal fluorescence microscopy. In this context, a field of independent random variables is observed, which is varying both in time and space. Each variable is a sum of two components, one following a Poisson and the other a Gaussian distribution. In this paper, a general formulation is considered where the associated Poisson process is nonstationary in space and also exhibits an exponential decay in time, whereas the Gaussian component corresponds to a stationary white noise with arbitrary mean. To solve the considered parametric estimation problem, we follow an iterative Expectation-Maximization (EM) approach. The parameter update equations involve deriving finite approximation of infinite sums. Expressions for the maximum error incurred in the process are also given. Since the problem is non-convex, we pay attention to the EM initialization, using a moment-based method where recent optimization tools come into play. We carry out a performance analysis by computing the Cramer-Rao bounds on the estimated variables. The practical performance of the proposed estimation procedure is illustrated on both synthetic data and real fluorescence macroscopy image sequences. The algorithm is shown to provide reliable estimates of the mean/variance of the Gaussian noise and of the scale parameter of the Poisson component, as well as of its exponential decay rate. In particular, the mean estimate of the Poisson component can be interpreted as a good-quality denoised version of the data.

/pdf/an-em-approach-for-time-variant-poisson-gaussian-model-58g8wienvh.pdf

An EM Approach for Time-Variant Poisson-Gaussian Model Parameter Estimation

The problem of detecting transient signals in noise under missing signal observations (samples) is considered. Specifically, a fusion center tries to detect the presence of a decaying signal in additive white Gaussian noise (AWGN) by collecting samples from distributed sensors through erasure channels, during which some of the samples may be lost. Under Neyman-Pearson detection, it is shown that missing samples cause performance degradation by reducing the signal energy received at the fusion center. Based on the assumption that the fusion center can control the sampling procedure through a feedback channel, an adaptive sampling policy is proposed with the goal of achieving accurate and timely detection with the minimum communication cost. The proposed policy is efficient and flexible in that it can be configured to yield a range of performance-cost combinations, where approximated closed-form solutions are derived for the configuration. Simulations show that compared with fixed-rate sampling, the proposed policy achieves significantly better tradeoff between detection performance and communication cost.

https://researcher.watson.ibm.com/researcher/files/us-the/HeZaferBisdikian08MILCOM.pdf

Detecting transient signals with incomplete observations

Cette these porte sur la restauration d'images degradees a la fois par un flou et par un bruit. Une attention particuliere est portee aux images issues de la microscopie confocale et notamment celles de macroscopie. Dans ce contexte, un modele de bruit Poisson-Gauss apparait bien adapte car il permet de prendre en compte le faible nombre de photons et le fort bruit enregistres simultanement par les detecteurs. Cependant, ce type de modele de bruit a ete peu exploite car il pose de nombreuses difficultes tant theoriques que pratiques. Dans ce travail, une approche variationnelle est adoptee pour resoudre le probleme de restauration dans le cas ou le terme de fidelite exact est considere. La solution du probleme peut aussi etre interpretee au sens du Maximum A Posteriori (MAP). L'utilisation d'algorithmes primaux-duaux recemment proposes en optimisation convexe permet d'obtenir de bons resultats comparativement a plusieurs approches existantes qui considerent des approximations variees du terme de fidelite. En ce qui concerne le terme de regularisation de l'approche MAP, des approximations discrete et continue de la pseudo-norme l₀ sont considerees. Cette mesure, celebre pour favoriser la parcimonie, est difficile a optimiser car elle est, a la fois, non convexe et non lisse. Dans un premier temps, une methode basee sur les coupures de graphes est proposee afin de prendre en compte des a priori de type quadratique tronque. Dans un second temps, un algorithme a memoire de gradient de type Majoration-Minimisation, dont la convergence est garantie, est considere afin de prendre en compte des a priori de type norme l₂-l₀. Cet algorithme permet notamment d'obtenir de bons resultats dans des problemes de deconvolution. Neanmoins, un inconvenient des approches variationnelles est qu'elles necessitent la determination d'hyperparametres. C'est pourquoi, deux methodes, reposant sur une approche Esperance-Maximisation (EM) sont proposees, dans ce travail, afin d'estimer les parametres d'un bruit Poisson-Gauss: (1) a partir d'une serie temporelle d'images (dans ce cas, des parametres de « bleaching » peuvent aussi etre estimes) et (2) a partir d'une seule image. De maniere generale, cette these propose et teste de nombreuses methodologies adaptees a la prise en compte de bruits et de flous difficiles, ce qui devrait se reveler utile pour des applications variees, au-dela meme de la microscopie

https://pastel.archives-ouvertes.fr/tel-00906718/document

Restauration d'images dégradées par un bruit Poisson-Gauss

Analysing data that consists of one or more truncated exponential functions is of great interest in a wide range of fields. Data consisting of one or more exponential functions are measured by a wide range of instruments, examples include: nuclear magnetic resonance, (NMR); magnetic resonance imaging (MRI); the analysis of radioactive decay data; chromatography; lifetime fluorescence imaging; spin resonance, (ESR). Many of the algorithms that are currently in used for 'characterising' this data, are difficult to interpret or are they useless and they produce erroneous results. Some of the key technical issues associated with this problem are discussed in this article we also present some results that use some elementary properties of exponential properties of exponential and harmonic functions. These properties allow us to develop relationships between these functions which we exploit to analyse exponentially decaying signals in noisy environments

Extracting useful information from noisy exponentially decaying signal

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

David Lawunmi

Author Tools

Chat about Author

Papers

Extracting useful information from noisy exponentially decaying signal