Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and...

Area of Science:

• Optics and Photonics
• Computer Vision
• Materials Science

Background:

• Multispectral imaging (MSI) captures spatial and spectral data simultaneously, enabling material composition analysis via reflection and absorption properties.
• Multi-aperture imaging offers snapshot capture of spectrally selective and spatially resolved scene information, but faces limitations in spectral resolution due to sensor and optical constraints.

Purpose of the Study:

• To present a systematic, universally applicable method for implementing multi-aperture MSI systems.
• To optimize MSI system configurations for specific applications by focusing on spectral system modeling, data analysis, and machine learning.

Main Methods:

• Developed a spectral system modeling approach to analyze MSI system properties.
• Utilized data analysis and machine learning techniques to create an adaptive MSI design loop.
• Tested the approach on a waste classification task to validate its effectiveness.

Main Results:

• Demonstrated a systematic methodology for designing and implementing multi-aperture MSI systems.
• Validated the approach's efficacy in optimizing sensor configurations for material classification tasks.
• Showcased the system's potential for real-world applications like waste sorting.

Conclusions:

• The presented systematic approach effectively addresses the challenges in multi-aperture MSI system implementation.
• This method facilitates the optimization of sensor configurations, enhancing material characterization capabilities.
• The framework provides a universally usable loop for application-driven MSI system development.