Robust Depth Estimation for Light Field Microscopy
Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/3/500 |
| _version_ | 1818000913222074368 |
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| author | Luca Palmieri Gabriele Scrofani Nicolò Incardona Genaro Saavedra Manuel Martínez-Corral Reinhard Koch |
| author_facet | Luca Palmieri Gabriele Scrofani Nicolò Incardona Genaro Saavedra Manuel Martínez-Corral Reinhard Koch |
| author_sort | Luca Palmieri |
| collection | DOAJ |
| description | Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization. |
| first_indexed | 2024-04-14T03:27:57Z |
| format | Article |
| id | doaj.art-36e6506abbfd4b01b48cc7e1c79b0857 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:27:57Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-36e6506abbfd4b01b48cc7e1c79b08572022-12-22T02:15:06ZengMDPI AGSensors1424-82202019-01-0119350010.3390/s19030500s19030500Robust Depth Estimation for Light Field MicroscopyLuca Palmieri0Gabriele Scrofani1Nicolò Incardona2Genaro Saavedra3Manuel Martínez-Corral4Reinhard Koch5Department of Computer Science, Christian-Albrecht-University, 24118 Kiel, GermanyDepartment of Optics, University of Valencia, E-46100 Burjassot, SpainDepartment of Optics, University of Valencia, E-46100 Burjassot, SpainDepartment of Optics, University of Valencia, E-46100 Burjassot, SpainDepartment of Optics, University of Valencia, E-46100 Burjassot, SpainDepartment of Computer Science, Christian-Albrecht-University, 24118 Kiel, GermanyLight field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization.https://www.mdpi.com/1424-8220/19/3/500depth estimationlight fieldmicroscopestereo matchingdefocus |
| spellingShingle | Luca Palmieri Gabriele Scrofani Nicolò Incardona Genaro Saavedra Manuel Martínez-Corral Reinhard Koch Robust Depth Estimation for Light Field Microscopy Sensors depth estimation light field microscope stereo matching defocus |
| title | Robust Depth Estimation for Light Field Microscopy |
| title_full | Robust Depth Estimation for Light Field Microscopy |
| title_fullStr | Robust Depth Estimation for Light Field Microscopy |
| title_full_unstemmed | Robust Depth Estimation for Light Field Microscopy |
| title_short | Robust Depth Estimation for Light Field Microscopy |
| title_sort | robust depth estimation for light field microscopy |
| topic | depth estimation light field microscope stereo matching defocus |
| url | https://www.mdpi.com/1424-8220/19/3/500 |
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