Overlapped block-based compressive sensing imaging on mobile handset devices

  • Henry Aguello-Fuentes Universidad Industrial de Santander
  • Irene Lizeth Manotas-Gutiérrez Universidad Industrial de Santander
Keywords: Compressive sensing, algoritmos de reconstrucción, dispositivos móviles

Abstract

Compressive Sensing (CS) is a new technique that simultaneously senses and compresses an image by taking a set of random projections from the underlying scene. An optimization algorithm is then used to recover the initial image. In practice, these optimization algorithms have restricted CS techniques to be implemented on high performance computational architectures, such as personal computers or graphical processing units (GPU) due the huge number of operations required for the image recovery. This work extends the application of CS to be implemented in an extremely limited memory and processing architecture such as a mobile device. Specifically, overlapped blocking-based algorithms are developed such that it is possible to reconstruct an image on a mobile device. An analysis of the energy consumption of the block-based CS algorithms is presented. The results show the required computational time for reconstruction and the image reconstruction quality for images of 128x128 and 256x256 pixels.

|Abstract
= 8 veces | PDF (ESPAÑOL (ESPAÑA))
= 7 veces|

Downloads

Download data is not yet available.

Author Biographies

Henry Aguello-Fuentes, Universidad Industrial de Santander

Profesor Asociado

Escuela de Ingeniería de Sistemas e Informática, Departamento de Sistemas e Informática

 

Irene Lizeth Manotas-Gutiérrez, Universidad Industrial de Santander

Departamento de Ingeniería de Sistemas e Informática

References

D. Donoho. “Compressed sensing”. IEEE Trans. On Information Theory. Vol. 52. 2006. pp. 1289- 1306.

T. Blumensath, M. Davies. “Gradient Pursuits”. IEEE Transactions on Signal Processing. Vol. 56. 2008. pp. 2370-2382.

E. Candes, T. Tao. “Near optimal signal recovery from random projections: Universal encoding strategies?”. IEEE Trans. On Information Theory. Vol. 52. 2006. pp. 5406-5425.

R. Calderban, S. Howard, S. Jafarpour. “Construction of a Large Class of Deterministic Sensing Matrices That Satisfy a Statistical Isometry Property”. IEEE Journal on Selected Topics in Signal Processing. Vol. 4. 2010. pp. 358-374.

L. Gan, T. Do, T. Tran. Fast compressive imaging using scrambled block Hadamard ensemble. Proceedings European Signal Processing Conference (EUSIPCO). Lausane, Switzerland. 2008. pp. 245.

T. Cai, G. Xu, J. Zhang, S. Member. “On Recovery of Sparse Signals Via l1 Minimization”. IEEE Transactions on Information Theory. Vol. 55. 2009. pp. 3388-3397.

S. Wright, R. Nowak, M. Figueiredo. “Sparse Reconstruction by Separable Approximation”. IEEE Transactions on Signal Processing. Vol. 57. 2008. pp. 2479-2493.

T. Blumensath, M. Davies. “Iterative hard thresholding for compressed sensing”. Applied and Computatational Harmonical Analysis. Vol. 27. 2009. pp. 265-274.

J. Tropp. “Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit”. IEEE Transactions on Information Theory. Vol. 53. 2007. pp. 4655-4666.

M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, R. Baraniuk. “Single-pixel imaging via compressive sampling”. IEEE Signal Processing Magazine. Vol. 25. 2008. pp. 83-91.

H. Arguello, G. Arce. “Code aperture optimization for spectrally agile compressive imaging”. Journal of the Optical Society of America (JOSA). Vol. 28. 2011. pp. 2400-2413.

H. Arguello, C. Correa, G. Arce. “Fast lapped reconstructions in compressive spectral imaging”. Journal Applied Optics. Vol. 52. 2013. pp. D32-D45.

M Balouchestani, K. Raahemifar, S. Krishnan. “Robust Wireless Sensor Networks with Compressing Sensing theory”. Springer Communication in Comp. and. Inf. Science. Vol. 293. 2012. pp. 608-619.

E. Correia, O. Postolache, P. Silva. “Implementation of compressed sensing in telecardiology sensor networks”. International journal of telemedicine and applications. Vol. 2010. pp. 1-12.

K. Kanoun, H. Mamaghanian, A. David. A realtime compressed sensing based personal electrocardiogram monitoring system. Design, Automation & Test in Europe Conference (DATE). Grenoble, France. 2011. pp. 1-6.

R. Willett, R. Marcia, J. Nichols. “Compressed sensing for practical optical imaging systems: a tutorial”. SPIE Optical Engineering. Vol. 50. 2011. pp. 586.

T. Blumensath. “Accelerated iterative hard thresholding”. IEEE Signal Processing Letters. Vol. 92. 2011. pp. 1-10.

H. Arguello, G. Arce. Restricted Isometry Property in Coded Aperture Compressive Spectral Imaging. IEEE Statistical Signal Processing Workshop. Ann Arbor, USA. 2012. pp. 716-719.

I. Gutierrez, H. Arguello, K. Winbladh. Implementation of Imaging Compressive Sensing Algorithms on Mobile Handset Devices. International Conference on Broadband and Wireless Computing, Communications and Applications. Victoria, Canada. 2012. pp. 252- 259.

P. Sermwuthisarn, S. Auethavekiat and V. Patanavijit. A Fast Image Recovery Using Compressive Sensing Technique with Block Based Orthogonal Matching Pursuit. International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). Tokyu, Kanazawa, Japan. 2009. pp. 212-215.

L. Gan. Block Compressed Sensing of Natural Images. International Conference in Digital Signal Processing. Cardiff, UK. 2007. pp. 403-406.

Published
2014-02-12
How to Cite
Aguello-Fuentes H., & Manotas-Gutiérrez I. L. (2014). Overlapped block-based compressive sensing imaging on mobile handset devices. Revista Facultad De Ingeniería Universidad De Antioquia, (70), 173-184. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/15284