Share Email Print
cover

Proceedings Paper

Adaptive compressive sensing camera
Author(s): Charles Hsu; Ming Kai Hsu; Jae Cha; Tomo Iwamura; Joseph Landa; Charles Nguyen; Harold Szu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We have embedded Adaptive Compressive Sensing (ACS) algorithm on Charge-Coupled-Device (CCD) camera based on the simplest concept that each pixel is a charge bucket, and the charges comes from Einstein photoelectric conversion effect. Applying the manufactory design principle, we only allow altering each working component at a minimum one step. We then simulated what would be such a camera can do for real world persistent surveillance taking into account of diurnal, all weather, and seasonal variations. The data storage has saved immensely, and the order of magnitude of saving is inversely proportional to target angular speed. We did design two new components of CCD camera. Due to the matured CMOS (Complementary metal–oxide–semiconductor) technology, the on-chip Sample and Hold (SAH) circuitry can be designed for a dual Photon Detector (PD) analog circuitry for changedetection that predicts skipping or going forward at a sufficient sampling frame rate. For an admitted frame, there is a purely random sparse matrix [Φ] which is implemented at each bucket pixel level the charge transport bias voltage toward its neighborhood buckets or not, and if not, it goes to the ground drainage. Since the snapshot image is not a video, we could not apply the usual MPEG video compression and Hoffman entropy codec as well as powerful WaveNet Wrapper on sensor level. We shall compare (i) Pre-Processing FFT and a threshold of significant Fourier mode components and inverse FFT to check PSNR; (ii) Post-Processing image recovery will be selectively done by CDT&D adaptive version of linear programming at L1 minimization and L2 similarity. For (ii) we need to determine in new frames selection by SAH circuitry (i) the degree of information (d.o.i) K(t) dictates the purely random linear sparse combination of measurement data a la [Φ]M,N M(t) = K(t) Log N(t).

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500W (29 May 2013); doi: 10.1117/12.2017881
Show Author Affiliations
Charles Hsu, The George Washington Univ. (United States)
Ming Kai Hsu, The George Washington Univ. (United States)
Jae Cha, Virginia Polytechnic Institute and State Univ. (United States)
Tomo Iwamura, Japan Ministry of Defense (Japan)
Joseph Landa, BriarTek, Inc. (United States)
Charles Nguyen, The Catholic Univ. of America (United States)
Harold Szu, The Catholic Univ. of America (United States)


Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top