RGB-2-Hyper-Spectral Image Reconstruction for Food Science Using Encoder/Decoder Neural Architectures
- Resource Type
- Conference
- Authors
- Williamson, Robert; Del Rincon, Jesus Martinez; Koidis, Anastasios; Reano, Carlos
- Source
- 2023 IEEE Symposium on Computers and Communications (ISCC) Computers and Communications (ISCC), 2023 IEEE Symposium on. :872-875 Jul, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Performance evaluation
Visualization
Systematics
Lighting
Imaging
Servers
Spatial resolution
Hyper-spectral image reconstruction
RGB
deep learning
GPU accelerators
- Language
- ISSN
- 2642-7389
Hyper-spectral imaging captures spatial and spectral information of a subject. This is used for the identification of substances within a scene, and food analysis. Presented is an investigation into the capabilities of encoder/decoder deep learning architectures for hyper-spectral image reconstruction from RGB images. For this analysis state-of-the-art (SOTA) techniques for hyper-spectral image reconstruction and other architectures from different fields have been used. Our approach examines a food science case study, using a CPU-based server and different accelerators. An in-house multi-sensor setup was used to capture the dataset which contains hyper-spectral images of twenty slices of different Spanish Ham in the range of 400-100∼nm and their analogous RGB images. The results show no degradation in the output when moving outside of the visual range. This study shows that the SOTA methods for reconstructing from RGB do not produce the most accurate reconstruction of the spectral domain within the range of 400-1000∼nm.