Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples
Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce dow...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1863 |
| _version_ | 1797618270036033536 |
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| author | Carlos Urbina Ortega Eduardo Quevedo Gutiérrez Laura Quintana Samuel Ortega Himar Fabelo Lucana Santos Falcón Gustavo Marrero Callico |
| author_facet | Carlos Urbina Ortega Eduardo Quevedo Gutiérrez Laura Quintana Samuel Ortega Himar Fabelo Lucana Santos Falcón Gustavo Marrero Callico |
| author_sort | Carlos Urbina Ortega |
| collection | DOAJ |
| description | Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications. |
| first_indexed | 2024-03-11T08:11:41Z |
| format | Article |
| id | doaj.art-420e989db1764fd48166adc8aa50774d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:11:41Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-420e989db1764fd48166adc8aa50774d2023-11-16T23:07:10ZengMDPI AGSensors1424-82202023-02-01234186310.3390/s23041863Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological SamplesCarlos Urbina Ortega0Eduardo Quevedo Gutiérrez1Laura Quintana2Samuel Ortega3Himar Fabelo4Lucana Santos Falcón5Gustavo Marrero Callico6European Space Agency, TEC-ED, 2201 AZ Noordwijk, The NetherlandsResearch Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainResearch Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainResearch Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainResearch Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainEuropean Space Agency, TEC-ED, 2201 AZ Noordwijk, The NetherlandsResearch Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainHyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.https://www.mdpi.com/1424-8220/23/4/1863hyperspectral imagingsuper-resolutionimage processingcomputational histologyremote sensing |
| spellingShingle | Carlos Urbina Ortega Eduardo Quevedo Gutiérrez Laura Quintana Samuel Ortega Himar Fabelo Lucana Santos Falcón Gustavo Marrero Callico Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples Sensors hyperspectral imaging super-resolution image processing computational histology remote sensing |
| title | Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples |
| title_full | Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples |
| title_fullStr | Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples |
| title_full_unstemmed | Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples |
| title_short | Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples |
| title_sort | towards real time hyperspectral multi image super resolution reconstruction applied to histological samples |
| topic | hyperspectral imaging super-resolution image processing computational histology remote sensing |
| url | https://www.mdpi.com/1424-8220/23/4/1863 |
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