Image Registration for the Alignment of Digitized Historical Documents

TL;DR

This paper surveys various image registration algorithms and evaluates their effectiveness for aligning digitized historical hyperspectral documents, selecting a cubic B-spline intensity-based approach with residual complexity and localized mutual information measures.

Contribution

It provides a comprehensive evaluation of registration algorithms for historical hyperspectral images and identifies the most suitable intensity-based method with specific similarity measures.

Findings

Residual complexity and localized mutual information are effective similarity measures.

Cubic B-splines effectively handle non-rigid deformations in hyperspectral images.

The selected registration approach performs well across various distortions.

Abstract

In this work, we conducted a survey on different registration algorithms and investigated their suitability for hyperspectral historical image registration applications. After the evaluation of different algorithms, we choose an intensity based registration algorithm with a curved transformation model. For the transformation model, we select cubic B-splines since they should be capable to cope with all non-rigid deformations in our hyperspectral images. From a number of similarity measures, we found that residual complexity and localized mutual information are well suited for the task at hand. In our evaluation, both measures show an acceptable performance in handling all difficulties, e.g., capture range, non-stationary and spatially varying intensity distortions or multi-modality that occur in our application.