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http://dx.doi.org/10.5573/ieie.2015.52.10.118

Spatially Scalable Kronecker Compressive Sensing of Still Images  

Nguyen, Canh Thuong (College of Information & Communication Engineering, Sungkyunkwan University)
Jeon, Byeungwoo (College of Information & Communication Engineering, Sungkyunkwan University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.10, 2015 , pp. 118-128 More about this Journal
Abstract
Compressive sensing (CS) has to face with two challenges of computational complexity reconstruction and low coding efficiency. As a solution, this paper presents a novel spatially scalable Kronecker two layer compressive sensing framework which facilitates reconstruction up to three spatial resolutions as well as much improved CS coding performance. We propose a dual-resolution sensing matrix based on the quincunx sampling grid which is applied to the base layer. This sensing matrix can provide a fast-preview of low resolution image at encoder side which is utilized for predictive coding. The enhancement layer is encoded as the residual measurement between the acquired measurement and predicted measurement data. The low resolution reconstruction is obtained from the base layer only while the high resolution image is jointly reconstructed using both two layers. Experimental results validate that the proposed scheme outperforms both conventional single layer and previous multi-resolution schemes especially at high bitrate like 2.0 bpp by 5.75dB and 5.05dB PSNR gain on average, respectively.
Keywords
compressive sensing; spatially scalable; Kronecker sensing; multi-resolution; total variation;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 K. Q. Dinh, H. J. Shim, and B. Jeon, "Measurement coding for compressive imaging based on structured measurement matrix," in Proc. IEEE Intern. Conf. on Image Process. (ICIP), pp. 10-13, Sep. 2013.
2 M. Duarte and R. Baraniuk, "Kronecker compressive sensing," IEEE Trans. Image Process., vol.21, no.2, pp. 494-504, Feb., 2012.   DOI   ScienceOn
3 T. Goldstein and S. Osher, "The split Bregman method for L1 regularized problems," SIAM J. on Imaging Sci., vol. 2, no. 2, pp. 323-343, Apr., 2009.   DOI
4 S. Shishkin, H. Wang, and G. Hagen, "Total variation minimization with separable sensing operator," in Proc. Conf. on Image and Signal Process.(ICISP), pp. 86-93, Jan., 2010.
5 T. N. Canh, K. Q. Dinh, and B. Jeon, "Detail preserving compressive sensing recovery based on cartoon texture image decomposition," in Proc. IEEE Inter. Conf. Imag. Process., (ICIP), pp. 1327 - 1331, Oct. 2014.
6 K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3D transform - domain collaborative filtering," IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, Aug., 2007.   DOI   ScienceOn
7 Y. Kim, H. Oh, and A. Bilgin, "Video compressed sensing using iterative self-similarity modeling and residual reconstruction," J. of Electron. Imaging, vol. 22. no.2, pp. 021005, Jan., 2013.   DOI
8 A. Sankaranarayanan, C. Studer, and R. Baraniuk, "CS-MUVI: Video compressive sensing for spatial-multiplexing cameras," in Proc. IEEE Inter. Conf. Computational Photography, pp. 1-10, Apr. 2012.
9 A. Sankaranarayanan, L. Xu, C. Studer, Y. Li, K. Kelly, and R. Baraniuk, "Video compressive sensing for spatial multiplexing cameras using motion-flow models," Avaiable at Arxiv.org (arXiv:1503.02727).
10 T. N. Canh, K. Q. Dinh, and B. Jeon, "Multi-resolution Kronecker Compressive Sensing," IEIE Trans. Smart. Sig. Comp., vol.3, no. 1, pp.19-27, Feb. 2014.
11 T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, "The STONE transform: multi-resolution image enhancement and real-time compressive video," Available at Arxiv.org (arXiv:1311.3405), 2013.
12 M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, "Single-pixel imaging via compressive sampling," IEEE Signal Process. Mag., vol. 25, pp. 83-91, Mar., 2008.   DOI   ScienceOn
13 S. Xiang and L. Cai, "Scalable video coding with compressive sensing for wireless videocast," in Proc. IEEE Inter. Conf. on Comm., pp. 1-5, Jun., 2011.
14 H. Jiang, C. Li, R. Haimi-Cohen, P. A. Wilford, and Y. Zhang, "Scalable video coding using compressive sensing," J. Bell Labs Tech., vol.16, no.4, pp.149-170, 2012.   DOI   ScienceOn
15 D. Valseia and E. Magli, "Spatially scalable compressed image sensing with hybrid transform and inter-layer prediction model," in Proc. IEEE Inter. Work. on Mul. Signal Process, pp. 373-378, Sept., 2013.
16 M. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, "A simple proof that random matrices are democratic", Available at Arxiv.org (arXiv:0911.0736v1), 2009.
17 A. Ashok and M. A. Neifeld, "Compressive imaging: hybrid measurement basis design," J Opt. Soc. Am. A, vol. 28, no. 6, pp. 1041-1050, Jun., 2011.   DOI   ScienceOn
18 Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, "Image quality assessment: From error measurement to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, 2004.   DOI   ScienceOn
19 S. Park, H. N. Lee, and S. Park, "Introduction to Compressed Sensing," The Magazine of the IEEK, vol. 38, pp. 19-30, Aug., 2011.
20 R. Baraniuk, M. Davenport, R. de Vore, and M. Wakin, "A simple proof of the Restricted Isometry Property for random matrices," Springer J. Constructive Approximation, vol. 28, no. 3, pp. 253-263, Jan., 2008.   DOI
21 H. Lee, S. Kwon, B. Shim, "Reweighted L1-Minimization via Support Detection," Journal of the Institute of Electronics Engineers of Korea, Vol. 48, no. 2, pp. 134-140, Mar. 2011.
22 S. Mun and E. Fowler, "Block compressed sensing of images using directional transforms," in Proc. IEEE Intern. Conf. on Image Process (ICIP), pp. 3021-3024, USA, 2009.
23 D. Donoho, "Compressed sensing," IEEE Trans. Info. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.   DOI   ScienceOn
24 J. Romberg, "Imaging via compressive sampling," IEEE Sig. Process. Mag., pp. 14-20, Mar. 2008.