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This series of images shows how a new design for a more energy-efficient digital camera can recreate an image with little initial information. The test image is on the left. The middle and right images are both composed of 15 percent of the information from the test image. What makes the image on the right look more like the one on the left are researchers' compressive sensing algorithms. Incorporating these algorithms into commercial cameras could allow a two-megapixel camera to snap 20-megapixel photos.
Credit: Kevin Kelly
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When a multi-megapixel digital camera snaps a shot, most of the information doesn't even make it into the final photo file. Indeed, about 90 percent of it is lost during the compression process that creates a JPEG file.
Collecting pixels just to throw them away is a wasteful process, says Richard Baraniuk, professor of electrical and computer engineering at Rice University--and it chews through a camera's battery life because compressing raw data is computationally demanding.
Baraniuk, Kevin Kelly, and colleagues at Rice are offering an alternative design, which they say makes for a more energy-efficient digital camera. Essentially, they've built and tested the hardware and software for a camera that collects just enough information to recreate a picture, while avoiding the traditional compression process.
In their prototype, the researchers used an array of tiny mirrors--a technology developed by Texas Instruments that's already used in high-definition projection televisions. The micromirror array takes in a small amount of information, and directs it onto a single sensor. Then algorithms are used to reconstruct the image. Since the prototype has only one sensor, in effect it's a single-pixel camera. However, the algorithm recreates an image with 100 times the resolution of what would traditionally be captured in a single pixel.
Baraniuk and his team recognized that an emerging field of information theory, called "compressive sensing," offered an alternative approach to conventional image acquisition and compression. Developed by researchers at Caltech, Stanford, the University of California, Los Angeles, and Rice, the technology is based on the idea that datasets, such as those that represent images or signals, often contain a significant amount of structure. When this structure is known, it can be used to extrapolate the image or signal when there's only a limited amount of available data. This concept of compressive sensing underlies the software for the researchers' digital camera.
To develop the camera's hardware that collects the image data, the Rice team turned to Texas Instruments' digital micromirror technology, which uses a collection of thousands of tiny mirrors that can be angled in two different directions. Facing one way, a mirror reflects the light from the scene onto the sensor, facing the other way it's dark. The mirrors are angled to transmit a pattern of light and dark onto the camera's sensor, flipping up to 100,000 times per second.
The orientation of each mirror is random, which is important, say the scientists, because it provides the best possible sampling for the algorithm to reconstruct the image. The random structure is known and fed into the algorithm. In all, only a few hundred samples projected onto the single pixel can provide enough information to reconstruct an image with tens or hundreds of thousands of pixels.
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Kate GreeneRead article at techreview.com