Image processing research is dominated, to a considerable degree, by linear-additive models of images. For example, wavelet decompositions are very popular both with experimentalists and theoreticians primarily because of their neatly convergent properties. Fourier and orthogonal series decompositions are also popular in applications, as well as playing an important part in the analysis of wavelet methods.
Multiplicative decomposition, on the other hand, has had very little use in image processing. In 1-D signal processing and communication theory it has played a vital part (amplitude, phase, and frequency modulations of communications theory especially).
In many cases 2-D multiplicative decompositions have just been too hard to formulate or expand. Insurmountable problems (divergences) often occur as the subtle consequences of unconscious errors in the choice of mathematical structure. In my work over the last 17 years I've seen how to overcome some of the problems in 2-D, and the concept of phase is a central, recurring theme. But there is still so much more to be done in 2-D and higher dimensions.
This talk will be a whirlwind tour of some main ideas and applications of phase in imaging.