Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm

One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.

Automated summary

This study proposed a portable and low-cost housing module based on snapshot hyperspectral imaging algorithm to differentiate between original and duplicate holograms. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system. A visible HSI algorithm was established to convert RGB images into hyperspectral images, and shorter wavelengths were found to be more suitable for differentiating holograms using mean gray value (MGV) as a parameter for classification.