Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2501.14287 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916880421224448 |
|---|---|
| author | Yang, Xilin Fanous, Michael John Chen, Hanlong Lee, Ryan Costa, Paloma Casteleiro Li, Yuhang Huang, Luzhe Zhang, Yijie Ozcan, Aydogan |
| author_facet | Yang, Xilin Fanous, Michael John Chen, Hanlong Lee, Ryan Costa, Paloma Casteleiro Li, Yuhang Huang, Luzhe Zhang, Yijie Ozcan, Aydogan |
| contents | Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. Here, we introduce a snapshot multi-spectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multi-spectral information; this encoded image information is rapidly decoded via a deep learning-based multi-spectral Fourier Imager Network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 92.98% for predicting the illumination channels at the input and achieved a robust multi-spectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multi-spectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Snapshot multi-spectral imaging through defocusing and a Fourier imager network Yang, Xilin Fanous, Michael John Chen, Hanlong Lee, Ryan Costa, Paloma Casteleiro Li, Yuhang Huang, Luzhe Zhang, Yijie Ozcan, Aydogan Optics Computer Vision and Pattern Recognition Machine Learning Applied Physics Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. Here, we introduce a snapshot multi-spectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multi-spectral information; this encoded image information is rapidly decoded via a deep learning-based multi-spectral Fourier Imager Network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 92.98% for predicting the illumination channels at the input and achieved a robust multi-spectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multi-spectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others. |
| title | Snapshot multi-spectral imaging through defocusing and a Fourier imager network |
| topic | Optics Computer Vision and Pattern Recognition Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2501.14287 |