Article in Optical Engineering covers compressive sensing algorithm for hyperspectral imaging.
Compressive sensing has been a research interest of mine for some time, and I have even recommended an article on the subject in this forum before. Understanding the elegant theory behind it gives a fuller picture of the broad subject of sampling.
That said, the application of compressive sensing to commercial systems is not widespread. I think that is in part because in many applications, detectors, memory, and bandwidth are cheap, allowing traditional methods of imaging and compression to be done at a relatively low cost.
One domain where this is distinctly not the case is hyperspectral imaging. While detectors are becoming relatively low cost, the volume of data that must be processed remains a serious performance factor for any hyperspectral system.
From this point of view, I found the April 2017 article in Optical Engineering, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressive sensing system” to be an informative read.
The authors are a team from Ben Gurion University of the Negev (Israel) who describe the innovative design used for their compressive sensing miniature ultraspectral imaging (CS-MUSI) sensor, which was demonstrated in a previous publication.
The point of this article, however, is the evaluation of the algorithm to be used with the sensor. To evaluate it, they use traditional hyperspectral data cubes and simulate the reduction that would be obtained with the proposed system.
Their evaluation indicates a tenfold reduction of data acquired by the sensor is possible with little loss of performance. The article is well written, well referenced, and lends itself to either a quick skim or a deep dive.
Authors are Daniel Gedalin, a master’s student; SPIE member Yaniv Oiknine, a PhD student and vice president of the SPIE Student Chapter at the university; Isaac August, a university graduate; Dan G. Blumberg, vice president and dean for R&D at Ben-Gurion; and SPIE Fellows Stanley R. Rotman and Adrian Stern, university professors. Stern is on the program committee for a conference on compressive sensing at SPIE Defense + Commercial Sensing in April.
Their article is part of a special section on optical computational imaging in Optical Engineering.
COMPRESSIVE SENSING CONFERENCE AT SPIE DCS
Compressive sensing is also the subject of a two-day conference during SPIE Defense + Commercial Sensing in April.
The conference will cover data/signal processing, sampling procedures, image reconstruction, and big data analytics.
SPIE Fellow Fauzia Ahmad of Temple University (USA) is conference chair. SPIE Fellow Adrian Stern of Ben-Gurion University (Israel) is on the program committee.
–SPIE Fellow Eddie L. Jacobs of University of Memphis is a member of the Optical Engineering editorial board.
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