Spectral Reflectance Recovery with Interreflection Using a Hyperspectral Image

Abstract

The capture of scene spectral reflectance (SR) provides a wealth of information about the material properties of objects, and has proven useful for applications including classification, synthetic relighting, medical imaging, and more. Thus many methods for SR capture have been proposed. While effective, past methods do not consider the effects of indirectly bounced light from within the scene, and the estimated SR from traditional techniques is largely affected by interreflection. For example, different lighting directions can cause different SR estimates. On the other hand, past work has shown that accurate interreflection separation in hyperspectral images is possible but the SR of all surface points needs to be known a priori. Thus we see that the estimation of SR and interreflection in its current form constitutes a chicken and egg dilemma. In this work, we propose the challenging and novel problem of simultaneously performing SR recovery and interreflection removal from a single hyperspectral image, and develop the first strategy to address it. Specifically, we model this problem using a compact sparsity regularized nonnegative matrix factorization (NMF) formulation, and introduce a scalable optimization algorithm on the basis of the alternating direction method of multipliers (ADMM). Our experiments have demonstrated its effectiveness on scenes with a single or two reflectance colors, containing possibly concave surfaces that lead to interreflection.