NMF-Based Spectral Reflectance Estimation From Image Pairs Including Near-Infrared Components

In this paper, a novel spectral reflectance estimation method from image pairs including near-infrared (NIR) components based on nonnegative matrix factorization (NMF) is presented. The proposed method enables estimation of spectral reflectance from only two kinds of input images: 1) an image including both visible light components and NIR components and 2) an image including only NIR components. These two images can be easily obtained using a general digital camera without an infrared-cut filter and one with a visible light-cut filter, respectively. Since RGB values of these images are obtained according to spectral sensitivity of the image sensor, the spectrum power distribution of the light source and the spectral reflectance, we have to solve the inverse problem for estimating the spectral reflectance. Therefore, our method approximates spectral reflectance by a linear combination of several bases obtained by applying NMF to a known spectral reflectance data set. Then estimation of the optimal solution to the above problem becomes feasible based on this approximation. In the proposed method, NMF is used for obtaining the bases used in this approximation from a characteristic that the spectral reflectance is a nonnegative component. Furthermore, the proposed method realizes simple approximation of the spectrum power distribution of the light source with direct and scattered light components. Therefore, estimation of spectral reflectance becomes feasible using the spectrum power distribution of the light source in our method. In the last part of this paper, we show some simulation results to verify the performance of the proposed method. The effectiveness of the proposed method is also shown using the method for several applications that are closely related to spectral reflectance estimation. Although our method is based on a simple scheme, it is the first method that realizes the estimation of the spectral reflectance and the spectrum power distribution of the light source from the above two kinds of images taken by general digital cameras and provides breakthroughs to several fundamental applications.