All That Matters

The temporal cloak. A light beam of a single colour is directed at a split-time lens (STL) that converts it into different colours. As the beam then propagates through an optical fibre, the blue light components travel faster than the red ones (the vertical axis shows time), so that eventually a brief gap is formed in the travelling beam during which there is no light present. Therefore, any even taking place during that temporal gap will be concealed from the beam. Afterwards, a reverse process restores the original beam so that an observer does not notice the cloaking device. Reprinted by permission from Macmillan Publishers Ltd. Nature (2012). doi:10.1038/nature10695

Devices that conceal objects from an observer are called cloaks. Conceptually, the idea of cloaking devices has its roots in science fiction, but such devices have indeed been demonstrated in the past few years. These cloaks are based on tiny structures that are able to bend light on predetermined paths as it passes through the structure. This is like a lens, but consisting of manmade materials, and much more versatile and powerful.

A very different type of cloak has now been published in Nature by Alexander Gaeta and colleagues from Cornell University. Following earlier theoretical proposals, they have now demonstrated the first temporal cloak where events are hidden in time, not in space so that an event is concealed from a light beam travelling through the same space for a certain amount of time. To understand the difference of a temporal to a spatial cloak, Robert Boyd and Zhimin Shi from the University of Rochester make a very good comparison in their News and Views article on the paper: […]

You may imagine vacuum as complete emptiness, as the very definition of nothing. But that’s not the case at all. Vacuum is humming with activity, as has now been demonstrated impressively in a study by researchers from Chalmers University of Technology in Göteborg, Sweden, RIKEN in Japan, the University of Michigan in the US and other institutions. They have created light basically out of nothing but the electromagnetic fields existing even in total vacuum.

Where does this contradiction come from that vacuum isn’t really ever empty? One of physics fundamental laws of physics, Heisenberg’s principle, states that even in vacuum virtual particles can exist. These are particles that appear only for such brief moments of time that they’re not noticeable to an observer. Indeed, vacuum is buzzing with all sorts of virtual particles and fields. One of Stephen Hawking’s most well-known predictions is that black holes emit light, which is an effect that relies on virtual particles. […]

The painting “Malacañang by the River” by the Philippines’ painter Fernando Amorsolo, before and after digital restoration. (c) Optical Express / Optical Society of America

The restoration of oil paintings is always a delicate process. Decades and centuries of dust and grime on the surface of a painting are difficult to remove, as the dirt sticks firmly to the painting’s oil paints and varnish. There is always the danger that a thorough physical cleaning and restoration may alter a painting’s original appearance.

A solution to restore a painting without touching it is to do it in the computer, which has the advantage that you can see the likely original state of a painting without restoration and its potentially damaging consequences. The problem here is that for digital restoration a reference is needed how the painting and the colours looked in their original state. This typically means that a small part of the painting has to be cleaned for real, or that some of the paint pigments are sampled from the painting and analysed. None of this is a good solution, as again they might affect the painting.

Cherry May T. Palomero and Maricor N. Soriano from the University of the Philippines have now developed an improved digital restoration technique based on a neural network algorithm. They took a 1948 painting by Fernando Amorsolo and took it out of its original frame. The sections of the painting that were covered by the wooden frame were less dirty than the exposed sections of the painting, and the researchers used these to train their neural network algorithm to clean up the painting.

The performance of the algorithm then needs to be checked for accuracy. For this, obvious facts such as that usually you would expect the sky in a painting to become more blue were used. And perhaps more relevant, the transition from the cleaner border of the painting to the main part should not be visible anymore once the algorithm is applied.

Nevertheless, an issue of using such computer algorithms is that they have a tendency to overdo it, with detrimental consequences. It is as if you crank up the contrast in a digital image manipulation and as a consequence important details of the painting are lost. To avoid this, Palomero anad Soriano record the colour distribution of specific sections of a painting before and after cleaning. If for some of the pixels the average colour distribution gets too distorted by the cleaning, then the process is reverted back for those pixels with the most extreme change in colour.

The result is a painting that certainly looks much cleaner and with more vibrant colours than the unrestored original. This way, the original painting can be enjoyed in all its splendour. And what’s more, should there ever be an actual restoration of the painting, then this digitally cleaned painting could serve as a template for the physical cleaning process.

Reference: (the paper is open access, so why not have a look)

Palomero, C., & Soriano, M. (2011). Digital cleaning and “dirt” layer visualization of an oil painting Optics Express, 19 (21) DOI: 10.1364/OE.19.021011