Link Functions and Training-Based in Reflectance Reconstruction from RGB Images

Recovering the spectral reflectance is important for object analysis and visualization. Previous approaches use either specialized equipment or controlled illumination where the extra hardware and high cost prevent many practical applications. In this paper, we focuses on a training-based method to reconstruct the scene’s spectral reflectance from RGB image. We use training images to model the mapping between camera-specific RGB values and scene-specific reflectance spectra. Our method is based on a radial basis function network that leverages RGB white-balancing to normalize the scene illumination and link function to transform the reflectance to recover the scene reflectance. Three link functions (logit, square root 1, square root 2) were evaluated in the training-based estimation of reflectance spectra of the RGB images in the 400–700 nm region. We estimate reflectance spectra from RGB camera responses in color patches’s reflectance reconstruction and a normal scene reconstruction and show that a combination of link function and radial basis function network training-based decreases spectral errors when compared with without link function model.