DIAR: Advances in Degradation Modeling and Processing
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Visual enhancement of old documents with hyperspectral imaging
Pattern Recognition
Restoration of X-ray fluorescence images of hidden paintings
Signal Processing
Multispectral image acquisition of ancient manuscripts
EuroMed'12 Proceedings of the 4th international conference on Progress in Cultural Heritage Preservation
Multispectral imaging of degraded parchment
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Can modern technologies defeat nazi censorship?
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
| Hi-index | 0.00 |
This paper reports some of the results obtained by applying statistical processing techniques to multispectral images of the Archimedes palimpsest. We focused on the possibilities of extracting the faint and highly degraded underwritten text, which constitutes the most ancient source for several treatises by Archimedes. Assuming each image to be generated by a linear mixture of different patterns, characterized by different emissivity spectra, the specific difficulty in separating the underwriting is that the mixture coefficients are unknown. To solve this problem, we rely on statistical techniques that maximize the information content of the processed images. In particular, we assessed the performances of the principal component analysis (PCA) and the independent component analysis (ICA) techniques. On the basis of 14 hyperspectral views of part of the palimpsest, we succeeded to extract clean maps of the primary Archimedes text, the overwritten text, and the mold pattern present in the pages. This goal was not reached in all the cases, because of the non-perfect adherence of the data model to reality. In most cases, however, PCA and ICA produced a significant enhancement of the underwritten text.