  Multispectral images contain many clues of surface characteristics of the
objects, thus can be widely used in many computer vision tasks, e.g.,
recolorization and segmentation. However, due to the complex illumination and
the geometry structure of natural scenes, the spectra curves of a same surface
can look very different. In this paper, a Low Rank Multispectral Image
Intrinsic Decomposition model (LRIID) is presented to decompose the shading and
reflectance from a single multispectral image. We extend the Retinex model,
which is proposed for RGB image intrinsic decomposition, for multispectral
domain. Based on this, a low rank constraint is proposed to reduce the
ill-posedness of the problem and make the algorithm solvable. A dataset of 12
images is given with the ground truth of shadings and reflectance, so that the
objective evaluations can be conducted. The experiments demonstrate the
effectiveness of proposed method.
