Enhancing spectral imaging with multi-condition image fusion
Abstract Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This sig...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84058-z |
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| author | Joana Teixeira Tomás Lopes Diana Capela Catarina S. Monteiro Diana Guimarães Alexandre Lima Pedro A. S. Jorge Nuno A. Silva |
| author_facet | Joana Teixeira Tomás Lopes Diana Capela Catarina S. Monteiro Diana Guimarães Alexandre Lima Pedro A. S. Jorge Nuno A. Silva |
| author_sort | Joana Teixeira |
| collection | DOAJ |
| description | Abstract Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission. To address these challenges, this work explores the potential of using techniques from conventional RGB imaging to enhance the dynamic range of spectral imaging. Drawing inspiration from multi-exposure fusion techniques, we propose an algorithm that calculates a global weight map using exposure and contrast metrics. This map is then used to merge datasets acquired with the same technique under distinct acquisition conditions. With case studies focused on LIBS and Raman Imaging, we demonstrate the potential of our approach to enhance the quality of spectral data, mitigating the impact of the aforementioned limitations. Results show a consistent improvement in overall contrast and peak signal-to-noise ratios of the merged images compared to single-condition images. Additionally, from the application perspective, we also discuss the impact of our approach on sample classification problems. The results indicate that LIBS-based classification of Li-bearing minerals (with Raman serving as the ground truth), is significantly improved when using merged images, reinforcing the advantages of the proposed solution for practical applications. |
| format | Article |
| id | doaj-art-a6ce558e6c0f4385bf07d5db0a2d9fb8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a6ce558e6c0f4385bf07d5db0a2d9fb82025-02-02T12:22:58ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84058-zEnhancing spectral imaging with multi-condition image fusionJoana Teixeira0Tomás Lopes1Diana Capela2Catarina S. Monteiro3Diana Guimarães4Alexandre Lima5Pedro A. S. Jorge6Nuno A. Silva7Centre for Applied Photonics, INESC TECCentre for Applied Photonics, INESC TECCentre for Applied Photonics, INESC TECCentre for Applied Photonics, INESC TECCentre for Applied Photonics, INESC TECDepartamento de Geociências, Ambiente e Ordenamento do TerritórioCentre for Applied Photonics, INESC TECCentre for Applied Photonics, INESC TECAbstract Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission. To address these challenges, this work explores the potential of using techniques from conventional RGB imaging to enhance the dynamic range of spectral imaging. Drawing inspiration from multi-exposure fusion techniques, we propose an algorithm that calculates a global weight map using exposure and contrast metrics. This map is then used to merge datasets acquired with the same technique under distinct acquisition conditions. With case studies focused on LIBS and Raman Imaging, we demonstrate the potential of our approach to enhance the quality of spectral data, mitigating the impact of the aforementioned limitations. Results show a consistent improvement in overall contrast and peak signal-to-noise ratios of the merged images compared to single-condition images. Additionally, from the application perspective, we also discuss the impact of our approach on sample classification problems. The results indicate that LIBS-based classification of Li-bearing minerals (with Raman serving as the ground truth), is significantly improved when using merged images, reinforcing the advantages of the proposed solution for practical applications.https://doi.org/10.1038/s41598-024-84058-z |
| spellingShingle | Joana Teixeira Tomás Lopes Diana Capela Catarina S. Monteiro Diana Guimarães Alexandre Lima Pedro A. S. Jorge Nuno A. Silva Enhancing spectral imaging with multi-condition image fusion Scientific Reports |
| title | Enhancing spectral imaging with multi-condition image fusion |
| title_full | Enhancing spectral imaging with multi-condition image fusion |
| title_fullStr | Enhancing spectral imaging with multi-condition image fusion |
| title_full_unstemmed | Enhancing spectral imaging with multi-condition image fusion |
| title_short | Enhancing spectral imaging with multi-condition image fusion |
| title_sort | enhancing spectral imaging with multi condition image fusion |
| url | https://doi.org/10.1038/s41598-024-84058-z |
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