  Sparse representation of 3D images is considered within the context of data
reduction. The goal is to produce high quality approximations of 3D images
using fewer elementary components than the number of intensity points in the 3D
array. This is achieved by means of a highly redundant dictionary and a
dedicated pursuit strategy especially designed for low memory requirements. The
benefit of the proposed framework is illustrated in the first instance by
demonstrating the gain in dimensionality reduction obtained when approximating
true color images as very thin 3D arrays, instead of performing an independent
channel by channel approximation. The full power of the approach is further
exemplified by producing high quality approximations of hyper-spectral images
with a reduction of up to 371 times the number of data points in the
representation.
