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Título
Multi-exposure microscopic image fusion-based detail enhancement algorithm
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Ultramicroscopy
Cita Bibliográfica
Singh, H., Cristobal, G., Bueno, G., Blanco, S., Singh, S., Hrisheekesha, P. N., & Mittal, N. (2022). Multi-exposure microscopic image fusion-based detail enhancement algorithm. Ultramicroscopy, 236, Article e113499. https://doi.org/10.1016/J.ULTRAMIC.2022.113499
Editorial
Elsevier
Fecha
2022
ISSN
0304-3991
Resumen
[EN] Traditional microscope imaging techniques are unable to retrieve the complete dynamic range of a diatom species with complex silica-based cell walls and multi-scale patterns. In order to extract details from the diatom, multi-exposure images are captured at variable exposure settings using microscopy techniques. A recent innovation shows that image fusion overcomes the limitations of standard digital cameras to capture details from high dynamic range scene or specimen photographed using microscopy imaging techniques. In this paper, we present a cell-region sensitive exposure fusion (CS-EF) approach to produce well-exposed fused images that can be presented directly on conventional display devices. The ambition is to preserve details in poorly and brightly illuminated regions of 3-D transparent diatom shells. The aforesaid objective is achieved by taking into account local information measures, which select well-exposed regions across input exposures. In addition, a modified histogram equalization is introduced to improve uniformity of input multi-exposure image prior to fusion. Quantitative and qualitative assessment of proposed fusion results reveal better performance than several state-of-the-art algorithms that substantiate the method’s validity
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MINECO/Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/CTM2014-51907-C2-R/ES/Desarrollo de métodos automáticos de identificación de diatomeas en el análisis cuantitativo y monitorización de la calidad de agua/AQUALITAS
- nfo:eu-repo/grantAgreement/Junta de comunidades de Castilla-La Mancha//SBPLY/19/180501/000273/ES/Definiendo la huella hiperespectral del cáncer de mama mediante técnicas de aprendizaje profundo aplicadas a imágenes microscópicas/HYPERDEEP
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