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Machine Learning Based Spectroscopy from RGB Images of Optical Phantoms
In this work, we investigate a cost-effective ML-based approach for reconstructing diffuse reflectance spectra from RGB images taken through a common mobile device.
Machine Learning Based Spectroscopy from RGB Images of Optical Phantoms
Mentor(s): Karthik Vishwanath
Introduction
In the field of physics, spectroscopy refers to the study of how matter interacts with light, revealing detailed information about the composition and properties of materials.
For example, biomedical imaging uses diffuse reflectance spectroscopy to analyze how biological tissue absorbs and scatters light, which provides insight into its composition and structure.
While very useful, a major limitation arises in diffuse spectroscopy experimentation: spectroscopic systems are traditionally reliant on expensive, specialized equipment, making research relatively inaccessible. Recent advances in machine learning have enabled new methods for extracting complex information from simple image data. In particular, RGB data from a single image contains implicit spectral information that can potentially be used to infer material properties.
In this work, we investigate a cost-effective ML-based approach for reconstructing diffuse reflectance spectra from RGB images taken through a common mobile device. To accomplish this, we construct optical phantoms using milk and food coloring to simulate variations in scattering and absorption properties, enabling repeatable and consistent experimentation for data collection and model training.