Fast terahertz reflection tomography using block-based compressed sensing

Image via Wikipedia

MY NOTE: I CAME ACROSS THIS ABSTRACT TONIGHT ON ONE OF THE WEB'S MOST INTERESTING BLOGS,  NUIT BLANCHE, BY DR. IGOR CARRON. I DON'T KNOW HOW MANY THz  COMPANIES ARE EXPLORING THE USE OF COMPRESSIVE SENSING TO ENHANCE TERAHERTZ IMAGING, BUT THE SMART & CREATIVE ONES WILL BE EMPLOYING IT, IN THE NEAR FUTURE IS MY BET.





http://nuit-blanche.blogspot.com/
by Sang-Heum Cho, Sang-Hun Lee, Chan Nam-Gung, Seoung-Jun Oh, Joo-Hiuk Son, Hochong Park, and Chang-Beom Ahn. The abstract reads:
In this paper, a new fast terahertz reflection tomography is proposed using block-based compressed sensing. Since measuring the time-domain signal on two-dimensional grid requires excessive time, reducing measurement time is highly demanding in terahertz tomography. The proposed technique directly reduces the number of sampling points in the spatial domain without modulation or transformation of the signal. Compressed sensing in spatial domain suggests a block-based reconstruction, which substantially reduces computational time without degrading the image quality. An overlap-average method is proposed to remove the block artifact in the block-based compressed sensing. Fast terahertz reflection tomography using the block-based compressed sensing is demonstrated with an integrated circuit and parched anchovy as examples.
__
BRIEFLY, IGOR HAS WRITTEN THE FOLLOWING INTRODUCTORY THOUGHT ABOUT COMPRESSIVE SENSING:

Compressed Sensing or Compressive Sensing is about acquiring a sparse signal in the most efficient way possible (subsampling) with the help of an incoherent projecting basis. Unlike traditional sampling methods, Compressed Sensing provides a new framework for sampling signals in a mutiplexed manner. The main theoretical findings in this recent field have mostly centered on how many multiplexed signals were necessary to  reconstruct the original signal and the attendant nonlinear reconstruction techniques needed to demultiplex these signals.  Another equally important thrust in the field has been the making of sensing hardware that could produce directly the multiplexed signals. (Read more here:

http://sites.google.com/site/igorcarron2/cs)
I ALSO FOUND THIS SITE VERY HELPFUL:
http://sunbeam.ece.wisc.edu/csaudio/

(HERE IS A SMALL BLURB):
Recently Professor Jordan Ellenberg wrote an article in Wired Magazine, 'Fill in the Blanks', describing how Compressed Sensing is being applied to signals such as MRI. In order to illustrate the concept, the article had a demonstration of how Compressed Sensing (CS) reconstruction works on an image of President Barack Obama.