Multi-exposure microscopic image fusion-based detail enhancement algorithm
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 microscop...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/36218 |
| Acceso en línea: | https://hdl.handle.net/10578/36218 |
| Access Level: | acceso abierto |
| Palabra clave: | Histogram equalization Image fusion Image decomposition Entropy |
| Sumario: | 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. |
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