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Visual enhancement of old documents with hyperspectral imaging
Joo Kim S., Deng F., Brown M. Pattern Recognition44 (7):1461-1469,2011.Type:Article
Date Reviewed: Nov 30 2011

Unlike regular color imaging that aims to represent a scene through a composition of the three fundamental colors (red, green, and blue), hyperspectral imagery (HSI) is identified as extreme since it is composed of tens or hundreds of grayscale bands, where each band (image) corresponds to the reflected light intensity within a narrow interval of wavelengths. In addition, HSI often covers an electromagnetic spectrum segment larger than that of regular human vision. The advantage here is that as we seek a more refined version of the scene we imaged, splitting the electromagnetic spectrum into ever-smaller intervals will allow us to detect ever-smaller differences among materials.

Only a decade or two ago, spectral imaging was seen as a field with few, albeit important, applications (agriculture and military, for example). Today, one can find such data used in a much wider context. For example, the authors use hyperspectral sensors in the analysis of documents, paintings (including the Mona Lisa), and other museum objects.

This paper provides a reasonable overview of the state of the art for HSI use in document imaging, as well as a discussion of the new contributions. It is basically a combination of experimental and algorithmic work aimed at artifact removal and enhancement of document imaging. The algorithms employ information from the nonvisible range to extract background information and perform a gradient-based transform that removes corrosion-related artifacts. The work focuses on both text and drawing, and, based on the qualitative evaluation of the imaging results, seems to provide considerable improvement over standard image enhancement.

Though the approach is not overly sophisticated or advanced, it seems sufficient for the type of applications for which it is intended. Overall, the paper provides an excellent example of HSI for paper-based documents.

Reviewer:  Stefan Robila Review #: CR139622 (1204-0414)
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