Researchers have found a clever way to extract hyperspectral data from everyday smartphone cameras; no bulky add-ons needed. The trick lies in a printed reference card paired with an algorithm that compensates for lighting and camera quirks. This lets a regular RGB image reveal full spectral signatures at high resolution, tells IEEE Spectrum.
Smartphone sensors are already sensitive beyond the red, green, and blue bands our eyes perceive. But historically, mapping that latent spectral information required either specialized hardware or tightly constrained machine-learning models tailored to specific scenes. Both approaches limit flexibility and general usage. The innovation here is a spectral color chart printed on a card placed in the frame. By including that known reference, the algorithm “normalizes” for light, camera response, and geometry, allowing recovery of subtle spectral variation (on the order of 1.6 nm) comparable to lab spectrometers. The possibilities are wide. Every molecule has a unique way of absorbing or reflecting different wavelengths. With hyperspectral data, phones could detect counterfeit whiskey, analyze pollutants in the air, scrutinize pigments in artworks, or even assist in medical diagnostics. Because the method uses only a printed card and software, it’s inexpensive and portable—ideas with potential in resource-limited settings.
However, challenges remain. Lighting changes, camera preprocessing, file formats, and real-world variability can all throw off spectral estimates. Past machine learning models struggled outside their training domains. This new method is more robust but still faces constraints when scenes deviate too far from calibration conditions.
This work reframes a photograph not just as color but as data. A simple tool plus smart processing could democratize hyperspectral imaging, making advanced sensing something anyone might carry in their pocket.