Identifying differences between very similar images
Identifying changes between two successive images is a necessary pre-condition of following change, and its significance. JMW Turner 1775-1851 (voted Britain’s most popular artist) produced upwards of 900 prints in his lifetime. One of the reasons for his reputation was his obsessive attention to refining the details of his prints. As a result, he and his engravers produced a succession of working proofs (states) for each print before it was published and a succession of different (usually deteriorating) versions for sale. Identifying these changes has, and is, traditionally done by placing two impressions of the print side by side, then looking for differences line by (microscopic) line. But that is really difficult to arrange when the pieces of paper are scattered across the world in institutional and private collections. Using the rapidly improving digital image analysis can provide a method of automatic comparison.
There are a number of difficulties. The process of making an impression from a metal plate involves humidifying the paper to ease the transfer of ink, then drying the impression, resulting in differential swelling and shrinkage which is different between different printings, papers and subsequent environmental conditions. Since their printing, many impressions have undergone mechanical damage, staining, or modification such as colouring. This makes comparisons somewhat more difficult. A trained human eye can learn to discount these distortions to follow individual features.
A first try at this problem by Anja le Blanc, see https://studiorum.itservices.manchester.ac.uk/print-comparisons/, used the strategy of aligning the two images using significant features on the images, such as a frame line and omitting the colour changes in the paper. Then cutting the images into 20 pixel squares and realigning using an average of the visible feature. Then identifying the lines/features in common. Then identifying and displaying the lines present/absent in one or the other image. This process was repeated in successive squares to cover the entire image. This was an excellent proof of concept, but threw up a number of problems. The major one is the difficulty of achieving good, i.e. perfect, alignment of the original printed lines in each square. The second one is ensuring that there is no mis-alignment between adjacent squares, which results from the initial alignment, and necessary distortion to align, of the initial images.
There are a number of requirements which require interim deliverables. The following is one suggested strategy, but the student may well come up with a better one.
- Identifying the features common to both images then placing them on a standardised grid. These features probably include every common printed line and point. In general, there are about 6 different “states” known for each print during development. So this standardised grid should be reusable, and refinable, for future comparisons.
- Normalising the images so that all the printed features have the same (or comparable) contrast with the background paper. Both the colour of the paper and the intensity of the ink colour can vary considerably, because of variability in the materials used initially and subsequent ageing, wear and tear. Again for a more general solution, it would be useful for all prints to be normalised to the same standard.
- Overlaying the images on the standardised grid and ensuring the maximum coincidence of features.
- Carrying out subtractions to show the features uniquely present and not present in each comparator image.
Reference Example Manipulaitons
High resolution images are available for all these. It would be useful to re-work the examples shown on the Turner webpages for comparison, These two impressions are nominally the same state, but look very different. Are they?
Reference Example Manipulations
High resolution images are available for all these. It would be useful to re-work the examples shown on the Turner webpages for comparison,
- These two impressions are nominally the same state, but look very different. Are they?
This is a successive sequence of images: 2a. R401_etc_cvh_cvh0140_PM