This paper summarizes a set of studies led on the topic of polarimetric coherence optimization for the coherent processing of stacks of polarimetric SAR images. It is shown that coherence maximization may be understood differently depending on the application at hand. Extracting polarimetric coherent signals embedded in noise or in a severe background requires to use polarimetric diversity as a supplementary mean for discovering organized speckles patterns, whereas classical MB-PolinSAR coherence optimization gives more importance to the polarimetric scattering mechanisms that extremize coherence values. This paper reviews different techniques able to cope with an arbitrary number of images and that are characterized by their low degree of computational complexity, conferred by the favored use of analytical solutions. The usefulness of these techniques is demonstrated using various kinds of applications to real spaceborne and airborne data sets.