| Title: |
SPECTRE: A Dataset for Spectral Reconstruction on Chip-Size Spectrometers with a Physics-Informed Augmentation Method |
| Authors: |
Wissing, Julio; Scholz, Teresa; Saloman, Stefan; Fargueta, Lidia; Junger, Stephan; Stefani, Alessio; Tschekalinskij, Wladimir; Scheele, Stephan; Schmid, Ute |
| Publication Year: |
2024 |
| Description: |
Reconstructing usable spectra from an array of optical filters has been central to miniature spectrometers for many years. However, as optical sensors become more suitable for low-cost applications by exploiting sub-par filter characteristics, creating high-quality reconstructions has become increasingly challenging. There has been growing interest in data-driven reconstruction methods within the scientific community to address this issue. The main obstacle to progress in this area is the lack of publicly available and comprehensive datasets for spectral reconstruction. In our research, we propose a physics-informed data augmentation technique to significantly increase the size of datasets for spectral reconstruction and make our augmented dataset, containing 10117 samples, publicly available. Furthermore, we demonstrate the potential performance improvements achieved by training complex neural networks for spectral reconstruction using the augmented dataset. Our short benchmark indicates a performance increase of up to four times over the baseline. |
| Document Type: |
conference object |
| Language: |
English |
| Relation: |
https://doi.org/10.1109/SENSORS60989.2024.10784898 |
| DOI: |
10.1109/SENSORS60989.2024.10784898 |
| Availability: |
https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/8118; https://doi.org/10.1109/SENSORS60989.2024.10784898 |
| Rights: |
http://www.gesetze-im-internet.de/urhg/__53.html ; info:eu-repo/semantics/closedAccess |
| Accession Number: |
edsbas.220F913C |
| Database: |
BASE |