To extract meaningful information from available data, researchers are often
confronted with data in some complex superposed state. Therefore, the
physical quantity of interest is not directly observable. In the physical
sciences, a common form of information mixing is linear superposition. This
includes fields as diverse as radio astronomy, Fourier transform spectroscopy,
atmospheric physics, and medical diagnostics. One problem confronting
researchers in these disciplines is restoring or deconvolving data.
In addition to simple data restoration, noise can complicate the restoration
process. This phenomenon can enter both prior to or during the collection of
data. Noise presents a major obstacle to perfect restoration, information gain,
and scientific understanding.