An iterative algorithm for spectral estimation with spatial smoothing

Many multispectral imaging systems are computational in nature and require processing of raw data in order to obtain radiance spectra. In this paper, we derive a fast and scalable spectral estimation algorithm based on the Alternating Direction Method of Multipliers (ADMM). Using this approach we solve for the unknown surface spectral reflectance simultaneously for all pixels in the image. This global formulation allows us to incorporate spatial as well as spectral regularizers, such as total variation penalty or non-negativity. We show that the estimates derived with our solver are more accurate and more robust in the presence of noise.