A Class of Algorithms for Fast Digital Image Registration

TLDR

Automatic determination of local similarity between two structured data sets is fundamental to pattern recognition and image processing. This paper introduces a class of algorithms that determine similarity far more efficiently than current methods. They employ translational image registration as an example, correcting issues with correlation‑based approaches, and are presented in simple implementations that illustrate their efficient structure. The new approach can reduce computation time by two orders of magnitude or more, and real ITOS‑1 satellite data confirm the theoretical predictions.

Abstract

The automatic determination of local similarity between two structured data sets is fundamental to the disciplines of pattern recognition and image processing. A class of algorithms, which may be used to determine similarity in a far more efficient manner than methods currently in use, is introduced in this paper. There may be a saving of computation time of two orders of magnitude or more by adopting this new approach. The problem of translational image registration, used for an example throughout, is discussed and the problems with the most widely used method-correlation explained. Simple implementations of the new algorithms are introduced to motivate the basic idea of their structure. Real data from ITOS-1 satellites are presented to give meaningful empirical justification for theoretical predictions.