  Multi-image alignment, bringing a group of images into common register, is an
ubiquitous problem and the first step of many applications in a wide variety of
domains. As a result, a great amount of effort is being invested in developing
efficient multi-image alignment algorithms. Little has been done, however, to
answer fundamental practical questions such as: what is the comparative
performance of existing methods? is there still room for improvement? under
which conditions should one technique be preferred over another? does adding
more images or prior image information improve the registration results? In
this work, we present a thorough analysis and evaluation of the main
multi-image alignment methods which, combined with theoretical limits in
multi-image alignment performance, allows us to organize them under a common
framework and provide practical answers to these essential questions.
