Illumination-targeting Data Augmentation for Monochrome Images

This paper addresses the challenge of data augmentation for monochrome images, which are still highly relevant in machine vision.Current augmentation methods designed for natural scenes focus on geometric transformation and often fall short in simulating real-world illumination variations.We propose a novel approach that leverages a two-step monochrome image processing technique consisting of image pseudo-colorization followed be a specific decolorization scheme.Our method generates diverse and unpredictable intensity variations, effectively emulating illumination changes and, if only a specific category of color maps are used, preserving the naturalness of the resulting images.To exclude the results which closely replicate images already in the dataset, we rank these maps, identifying a subset that significantly alters illumination characteristics of the originals.A popular SSIM measure is used for that purpose.The proposed technique enhances the illumination diversity of datasets, offering a valuable tool for improving the robustness of AI/ML models in monochrome imagery.NOTE: All figures are best viewed in high resolution.