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Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]

Romberg, Justin (2008) Imaging via Compressive Sampling [Introduction to compressive sampling and recovery via convex programming]. IEEE Signal Processing Magazine, 25 (2). pp. 14-20. ISSN 1053-5888. doi:10.1109/MSP.2007.914729.

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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.

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Additional Information:© Copyright 2008 IEEE. Reprinted with permission. Posted online: 2008-03-21.
Issue or Number:2
Record Number:CaltechAUTHORS:ROMieeespm08
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10093
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Deposited On:11 Apr 2008
Last Modified:08 Nov 2021 21:05

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