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dc.contributor.author
Martinez, Jorge Alberto
dc.contributor.author
Pistonesi, Silvina
dc.contributor.author
Maciel, Maria Cristina
dc.contributor.author
Flesia, Ana Georgina
dc.date.available
2021-02-05T19:49:37Z
dc.date.issued
2019-10
dc.identifier.citation
Martinez, Jorge Alberto; Pistonesi, Silvina; Maciel, Maria Cristina; Flesia, Ana Georgina; Multi-scale fidelity measure for image fusion quality assessment; Elsevier Science; Information Fusion; 50; 10-2019; 197-211
dc.identifier.issn
1566-2535
dc.identifier.uri
http://hdl.handle.net/11336/125014
dc.description.abstract
Image fusion is considered an effective enhancing methodology widely included in high-quality imaging systems. Nevertheless, like other enhancing techniques, output quality assessment is made within small sample subjective evaluation studies which are very limited in predicting the human-perceived quality of general image fusion outputs. Simple, blind, universal and perceptual-like methods for assessing composite image quality are still a challenge, partially solved only in particular applications. In this paper, we propose a fidelity measure, called MS-Q W with two major characteristics related to natural image statistics framework: A multi-scale computation and a structural similarity score. In our experiments, we correlate the scores of our measure with subjective ratings and state of the art measures included in the 2015 Waterloo IVC multi-exposure fusion (MEF) image database. We also use the measure to rank correctly the classical general fusion methods included in the Image Fusion Toolbox for medical, infra-red and multi-focus image examples. Moreover, we study the scores variability and statistical discrimination power with the TNO night vision database using the Friedman test. Finally, we define a new leave one out procedure based on our fidelity measure that selects the best subset of images (within a collection of distorted and unregistered cell phone type images) that provides a defect-free composite output. We exemplify the procedure with the fusion of a collection of images from Latour and Van Dongen paintings suffering from glass highlights and speckle noise, among other artifacts. The proposed multiscale quality measure MS-Q W demonstrates improvement over the previous single-scale similarity measures towards a fidelity assessment between quantitative image fusion quality metrics and human perceptual qualitative scores.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
HIGH QUALITY PHOTOGRAPHS OF PAINTINGS
dc.subject
IMAGE FUSION QUALITY ASSESSMENT
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MULTI-SCALE MEASURES
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STATISTICAL PERFORMANCE ASSESSMENT
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STRUCTURAL SIMILARITY
dc.subject.classification
Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Multi-scale fidelity measure for image fusion quality assessment
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-19T21:20:45Z
dc.journal.volume
50
dc.journal.pagination
197-211
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Martinez, Jorge Alberto. Universidad Nacional del Sur; Argentina
dc.description.fil
Fil: Pistonesi, Silvina. Universidad Nacional del Sur; Argentina
dc.description.fil
Fil: Maciel, Maria Cristina. Universidad Nacional del Sur; Argentina
dc.description.fil
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
dc.journal.title
Information Fusion
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.inffus.2019.01.003
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1566253518301362?via%3Dihub
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