Fast Spectral Data Simulation from sRGB Images Using K-Dimensional Search Trees

Abstract Spectral imaging is a powerful tool to analyse and manipulate the spectral content of a scene in a spatial context. Nevertheless, it is a complex and expensive tool, and limited under certain conditions (non-ambient temperature conditions, low light etc.). This study introduces a fast and efficient alternative to generate plausible spectral data from the CIELAB values of an sRGB image and a spectral reflectance database of +7 million colours. The proposed methodology relies on the compartmentalization capabilities of K-Dimension search trees to quickly identify, under a chosen light source, the spectral reflectance sample, that most closely matches the La*b* values of each test pixel. On average, the algorithm takes 6 s to substitute RGB values with plausible spectral data for full size high-definition images. The developed technique allows lighting or imaging researchers to rapidly generate spectral data directly from RGB images. The simulated spectral data, when, converted to CIELAB values, produces an image perceptually identical to the original LAB values for 95% pixels (<2 Delta Eab*). Moreover, by simulating spectral data under actual light source, researchers can achieve high spectral accuracy comparable to actual spectral data.