Optimizing Color Vision Accuracy in Ishihara Tests through Spectral Illumination Optimization

In order to design optimal illumination that minimizes or maximizes differences between two sample points, we utilized particle swarm optimization (PSO), a popular algorithm for finding optimal solutions. In this study, we explored the application of PSO to minimize or maximize cost functions such as ∆E, ∆RGB, and Michelson contrast, and to output optimal intensities for designing the emission spectra of the illumination. This approach proved to be more convenient than altering the spectral properties of the object or the observer. The experiment involved capturing sample data from the Ishihara Color Blindness test book using Specim V10E, and measuring the emission spectra of ten LEDs from Ledmotive’s spectrally tunable LED light source using the Hamamatsu PMA-12 spectrometer. Subsequently, we pre-processed the captured data and applied PSO to obtain optimal intensities for the ten LEDs. The resulting optimized intensities were then used to compute the spectrally optimal emission spectrum. To assess the performance of these emission spectra, we illuminated the samples with the optimized spectra using the spectrally tunable LED light source. From the illuminated samples, we concluded that for the minimum cost function, the red number on the sample was not distinguishable from the green background, whereas for the maximum cost function, the red number was distinctive.