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http://dx.doi.org/10.6109/jkiice.2016.20.10.1961

A Compressive Sensing Based Imaging Algorithm Using Incoherent Measurements and DCT  

Kim, Seehyun (Department of Information and Communications Engineering, The University of Suwon)
Abstract
Compressive sensing has proved that a signal can be restored from less samples than the Nyquist rate. Reducing the required data rate is essential for a variety of fields including compression, transmission, and storage. It has been made lots of attempt to apply the compressive sensing theory into data intensive fields, such as image processing which needs to cover 4K and 8K pictures. In this paper, an image acquisition algorithm based on compressive sensing is proposed. It combines DCT, which can compact the energy of a image into a few coefficients, and the Noiselet transform, which is incoherent with DCT. The DCT coefficients represent the coarse structure of the images while the Noiselet information holds the fine details. Performance experiments with several images show that the proposed image acquisition algorithm not only outperforms the previous results, but also improves the reconstruction quality faster as the number of measurements increases.
Keywords
Image Acquisition; Compressive Sensing; DCT; Noiselet; SOCP (second order cone program);
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  • Reference
1 W. Pratt, Digital Image Processing, 4th Ed., Wiley-Interscience, New Jersey, 2007.
2 A. Skodras, C. Christopoulos, and T. Ebrahimi, "The JPEG2000 still image compression standard," IEEE Signal Processing Magazine, vol. 18, no. 9, pp. 36-58, Sep. 2001.   DOI
3 E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans., Information Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.   DOI
4 D. Donoho, M. Elad, and V. Temlyakov, "Stable recovery of sparse overcomplete representation in the presence of noise," IEEE Trans., Information Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006.   DOI
5 E. Candes, J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Prob., vol. 23, no. 3, pp. 969-986, June 2007.   DOI
6 E. Lam and J. Goodman, "A mathematical analysis of the DCT coefficient distributions for images," IEEE Trans., Image Processing, vol. 9, no. 10, pp. 1661-1664, Oct. 2000.   DOI
7 S. Mallat, Digital A Wavelet Tour of Signal Processing, 2nd Ed., Academic, 1999.
8 R. Coifman, F. Geshwind, and Y. Meyer, "Noiselets," Applied and Computational Harmonics Analysis, vol. 10, no. 1, pp. 27-44, January 2001.   DOI
9 S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge Univ. Press, New York, 2004.
10 J. Romberg, "Imaging via compressive sampling," IEEE Signal Processing Magazine, vol. 25, pp. 14-29, March 2008.