Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples

March 26, 2010

A recent article in Wired Magazine covers compressed sensing, describing it as "the paradigm-busting field in mathematics that's reshaping the way people work with large data sets" and "the hottest topic in applied math today."

Compressed sensing was discovered by chance in 2004 when Caltech mathematician Emmanuel Candès used a mathematical technique called l₁ minimization to help clear up a blurry image. The results were amazing. The algorithm rendered the image with remarkable clarity.

Candès presented his ideas to UCLA colleague Terrance Tao and their conversation formed the basis of their first paper together, "Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?"

The article was written by mathematician and blogger Jordan Ellenberg (University of Wisconsin). In the article he describes the capabilities of compressed sensing.

"Imagine MRI machines that take seconds to produce images that used to take up to an hour, military software that is vastly better at intercepting an adversary’s communications, and sensors that can analyze distant interstellar radio waves," Ellenberg wrote. "Suddenly, data becomes easier to gather, manipulate, and interpret."

Read the full article here.

Source: Wired Magazine (Feb. 22, 2010)