Paper Conservation: Decisions & Compromises

ther because the user failed to focus the camera
tightly enough, or because the camera was too
far away from the subject (a possible restriction
due to limited gutter opening space for bound
collection items). Unsharp mask is a command
in the ImageJ Process menu which subtracts a
blurred duplicate of the image followed by a
rescaling of the image to obtain the same contrast
of large low-frequency structures as in the
original. The image is sharpened as this process
is equivalent to adding a high-pass filtered image
to the input image.
Unsharp mask is the final operation which
should be applied to an image during processing.
It is a destructive process. Images can also
be sharpened using Process > Sharpen but the
user has no parameter control (blur radius and
strength of filtering). Unsharp mask changes
colour values and is especially destructive in the
fine gradations of colour which form part of the
image that is most vital in enhancement processes.
Smooth backgrounds become grainy and lose
subtle colour definition crucial to restoration,
while boundaries become more well-defined
(Fig. 4).
Conclusion
A suite of processing and analysing features in
the ImageJ package offers multiple applications
to the field of cultural heritage including image
file distribution, digital image enhancement,
metadata generation, watermark extraction,
colour management, provenance determination,
degradation monitoring, artifact interpretation,
scholarly understanding and publication, and
boundary definition. The increase in digitisation
projects generates large volumes of image files
that can be processed to enhance understanding
of our collections, without physically handling
fragile material.
References
Barilla, M.E. of the Digital Systems & Vision Processing
Group at The University of Birmingham, UK is the author
of the ImageJ plugin ‘Color Transformer’. The first
version was released in 2007 and was last updated in
2012. http://rsbweb.nih.gov/ij/plugins/color-transforms.
html.
Faigenbaum, S, Sober, B., Shaus, A., Moinester, M., Piasetzky,
E., Bearman, G., Cordonsky, M., and Finkelstein, I.,
Multispectral images of ostraca: acquisition and analysis,
Journal of Archaeological Science, Volume 39, Issue
12, December 2012, Pages 3581-3590.
McFeely, C., An introduction to digital restoration techniques
using high resolution digital images, The DIAMM
Digital Restoration Workbook, 2006.
Schwartzwald, D is the author of the ImageJ plugin
‘Color Space Converter’. The first version was released in
2006 and was last updated in 2007.
Figure Captions
Fig. 1: Brightness and Contrast. (a): original image, (b):
brightness and contrast adjusted image. The image is
processed using the automatic brightness and contrast
command which optimises the settings based on the image’s
histogram. (Image: Cotton MS Nero D.IV Folio 89v).
Fig. 2: Merging and Splitting Channels. (a): multispectral
image at 420 nm, (b) multispectral image at 720 nm, (c):
multispectral image at 1,000 nm, (d): pseudocolour image
which is the sum of (a), (b) and (c) where colours are
assigned as (a) = red, (b) = green and (c) = blue. (Image:
Add. Ms. 45722, Leaf from Sforza Hours).
Fig. 3: Rotation and Transformation. (a): unrotated original
image of the letter a, (b): rotated image of the letter
a, (c): unrotated close-up of the letter a showing pixel
distribution, (d): rotated close-up of the letter a showing
increased pixel blur when compared to (c). (Image: Cotton
Augustus ii 106).
Fig. 4: Unsharp Mask: (a): original unsharpened image,
(b): image after Unsharp Mask command is applied
once, (c): unsharp mask command is applied twice, (d):
unsharp mask is applied three times. (Image: Add. Ms.
45722, Leaf from Sforza Hours).
Author
Christina Duffy
The British Library, 96 Euston Road, London, NW1 2DB,
United Kingdom, christina.duffy@bl.uk.
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