Sharpening up the blur

The conventional wisdom that a machine is only as good as its parts may no longer apply to cameras and other imaging devices.

An engineer at the University of California, Santa Cruz, has found a fast way to take blurry, imprecise pictures and turn them into crisp, clear images by using software to ‘guess’ what the picture really should be.

His work may have a wide range of applications, from improving surveillance cameras to building quality control monitors for tiny electronic devices. Eventually, this ‘superresolution’ technique may enable ordinary, inexpensive video cameras to produce clean, high-quality images.

Peyman Milanfar, an assistant professor of electrical engineering at UCSC is working with Nhat Nguyen at KLA-Tencor Corporation in Milpitas and Gene Golub of Stanford University to develop the technique.

A digital camera is equipped with a grid of ‘charge-coupled devices’ (CCDs). Each CCD element corresponds to a single pixel, or dot, of the image. So the density of its CCD grid limits a camera’s resolution.

But Milanfar has found a way to stretch that limit. The idea is to start by taking not one photo of the object you want to capture, but several, each one from a slightly different vantage point. Then collect all the pixels of information from these different shots, and use a computer program to figure out what image must have created those several shots.

That’s easier said than done. If a computer were to do the calculation directly, then improving the resolution of a typical photo by a factor of four would entail solving a system of equations with more than a million unknowns. To get around this difficulty, Milanfar uses interpolation techniques to shortcut the calculation.

Techniques for achieving superresolution of photos have been around for about a decade, but their extreme slowness has prevented them from being used in practical applications.

Milanfar’s new method is about 10 times faster than its fastest competitor, he said, but it is still short of the speed necessary for real-time applications. He is working to improve his technique’s performance even further.

‘From a practical point of view, we want to have a device that can do this on the fly,’ Milanfar said. ‘So you’ll buy your cheap camera, plug it into a circuit that has our algorithm on a chip, and on the other end you’ll get high-resolution images.’

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