A Practical Guide to Multi-image Alignment

TL;DR

This paper provides a comprehensive analysis and comparison of multi-image alignment methods, addressing practical questions about their performance, limitations, and conditions for optimal use, supported by theoretical insights.

Contribution

It offers a unified framework for evaluating multi-image alignment algorithms and answers key practical questions through thorough analysis and empirical evaluation.

Findings

Performance varies across methods depending on conditions

Adding more images or prior information can improve results

Theoretical limits help identify potential for improvement

Abstract

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.