Mirrored mixture PDF models for scientific image modelling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43925510" target="_blank" >RIV/60461373:22340/22:43925510 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/22:00350430

Result on the web

<a href="https://link.springer.com/article/10.1007/s11760-021-01944-z" target="_blank" >https://link.springer.com/article/10.1007/s11760-021-01944-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11760-021-01944-z" target="_blank" >10.1007/s11760-021-01944-z</a>

Result language

angličtina

Original language name

Mirrored mixture PDF models for scientific image modelling

Original language description

This paper deals with the modelling of high bit-depth images acquired by astronomical cameras using the discrete wavelet transform and the undecimated discrete wavelet transform for image representation. The probability density function (PDF) model parameters are estimated using the expectation-maximization (EM) algorithm and the method of moments. As proposed in this paper, the task of estimating the overall PDF model parameters can be simplified by so-called mirroring of the initial model which is estimated only for those wavelet coefficients that are greater than or equal to zero. In the case of the EM algorithm, this technique significantly reduces the computational cost of the model fitting algorithm. In our experiments, we achieved a reduction of more than 70%. In the case of the method of moments, this technique simplifies a system of moment equations. Three main PDF models are presented here: firstly, the mirrored mixture of a half-normal distribution and an exponential distribution, secondly, the mirrored mixture of two exponential distributions, and finally, the mirrored mixture of two half-normal distributions. Performance of these models is evaluated on three sets of astronomical images and also on artificial data using the Jeffrey divergence metric. Overall, the mirrored mixture of a half-normal and an exponential distribution overcomes the commonly used GLM (generalized Laplacian model) and also the other studied models. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-05840S" target="_blank" >GA17-05840S: Multicriteria Optimization of Shift-Variant Imaging System Models</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Signal, Image and Video Processing

ISSN

1863-1703

e-ISSN

1863-1711

Volume of the periodical

16

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

385-393

UT code for WoS article

000660811100002

EID of the result in the Scopus database

2-s2.0-85107821204