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Multispectral imaging is an important technique for improving the readabilityof written or printed text where the letters have faded, either due todeliberate erasing or simply due to the ravages of time. Often the text can beread simply by looking at individual wavelengths, but in some cases the imagesneed further enhancement to maximise the chances of reading the text. There aremany possible enhancement techniques and this paper assesses and compares anextended set of dimensionality reduction methods for image processing. Weassess 15 dimensionality reduction methods in two different manuscripts. Thisassessment was performed both subjectively by asking the opinions of scholarswho were experts in the languages used in the manuscripts which of thetechniques they preferred and also by using the Davies-Bouldin and Dunn indexesfor assessing the quality of the resulted image clusters. We found that theCanonical Variates Analysis (CVA) method which was using a Matlabimplementation and we have used previously to enhance multispectral images, itwas indeed superior to all the other tested methods. However it is very likelythat other approaches will be more suitable in specific circumstance so wewould still recommend that a range of these techniques are tried. Inparticular, CVA is a supervised clustering technique so it requiresconsiderably more user time and effort than a non-supervised technique such asthe much more commonly used Principle Component Analysis Approach (PCA). If theresults from PCA are adequate to allow a text to be read then the added effortrequired for CVA may not be justified. For the purposes of comparing thecomputational times and the image results, a CVA method is also implemented inC programming language and using the GNU (GNUs Not Unix) Scientific Library(GSL) and the OpenCV (OPEN source Computer Vision) computer vision programminglibrary.

High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts