Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]
- Creators
- Romberg, Justin
Abstract
There is an extensive body of literature on image compression, but the central concept is straightforward: we transform the image into an appropriate basis and then code only the important expansion coefficients. The crux is finding a good transform, a problem that has been studied extensively from both a theoretical [14] and practical [25] standpoint. The most notable product of this research is the wavelet transform [9], [16]; switching from sinusoid-based representations to wavelets marked a watershed in image compression and is the essential difference between the classical JPEG [18] and modern JPEG-2000 [22] standards. Image compression algorithms convert high-resolution images into a relatively small bit streams (while keeping the essential features intact), in effect turning a large digital data set into a substantially smaller one. But is there a way to avoid the large digital data set to begin with? Is there a way we can build the data compression directly into the acquisition? The answer is yes, and is what compressive sampling (CS) is all about.
Additional Information
© Copyright 2008 IEEE. Reprinted with permission. Posted online: 2008-03-21.Files
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Additional details
- Eprint ID
- 10093
- Resolver ID
- CaltechAUTHORS:ROMieeespm08
- Created
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2008-04-11Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field