CS Compressed Sensing
CS Compressed Sensing, it's been around for decades,
but 4 scientists have brought it back into the limelight.
Another way of saying what it does is "Less Can Mean More."
I learned about it in my latest issue of Wired Magazine. The reason I get Wired is for pieces like this. Not for their idea of the "Best SF Movies of All Time" which is churlish to say the least. I've wanted to use that word for some time now, and there was my chance.
Anyway, back to CS.
The article in the 1803 (It means March) issue was on page 62 was titled: "Fill In The Blanks" by Jorden Ellenberg who stated: A revolutionary algorhythm can make something out of nothing.
The touching story at the begining of the piece had to do with an MRI that needed to be done in Less time than was needed to get a highly detailed reading of a problem liver. They needed a full two minutes. The young patient had to be under anesthesia to keep from breathing during the scan.
By trying an alogorithm of CS they were able to get a scan in 40 seconds that could be computationally expanded to give the detail of a two minute scan.
The algorithm stated that if there are a million ways to reconstruct an image, the simplest option is always the best.
Take a picture of say 1 million pixels. That requires a million measurements to reconstruct. In CS, take a subset of that, like 100,000 randomly selected pixels from various parts of the image. From that, there is an infinite number of ways to fill-in the missing 900,000.
The way to come up with the right answer is something in mathematics called sparsity, which describes the complexity or lack thereof of the image.
That my friends is the tip of the iceberg of this new tool to create more out of less.
Read the article.
Wikipedia Logo:
Wikipedia had this opening line to say about it:
Compressed Sensing, also known as compressive sensing, compressive sampling and sparse sampling, is a technique for acquiring and reconstructing a signal utilizing the prior knowledge that it is sparse or compressible. The field has existed for at least four decades, but recently the field has exploded, in part due to several important results by David Donoho, Emmanuel Candès, Justin Romberg and Terence Tao.
Wikipedia Link: http://en.wikipedia.org/wiki/Compressed_sensing
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Wingman, a Calculating kind of guy.
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