Browse > Article
http://dx.doi.org/10.9717/kmms.2016.19.2.200

An Effective Fast Algorithm of BCS-SPL Decoding Mechanism for Smart Imaging Devices  

Ryu, Jung-seon (Dept. of Multimedia Eng., Graduate School of Info. & Comm., Hanbat National University)
Kim, Jin-soo (Dept. of Info. & Comm. Eng., Hanbat National University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.2, 2016 , pp. 200-208 More about this Journal
Abstract
Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. A block compressed sensing with projected Landweber (BCS-SPL) framework is most widely known, but, it has high computational complexity at decoder side. In this paper, by introducing adaptive exit criteria instead of fixed exit criteria to SPL framework, an effective fast algorithm is designed in such a way that it can utilize efficiently the sparsity property in DCT coefficients during the iterative thresholding process. Experimental results show that the proposed algorithm results in the significant reduction of the decoding time, while providing better visual qualities than conventional algorithm.
Keywords
Compressed Sensing; BCS-SPL; Fast BCS-SPL; Iterative Thresholding; Sparsity; DCT;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 L. Xin, Z. Junguo, C. Chen, and L. Fantao "Adaptive Sampling Rate Assignment for Block Compressed Sensing of Images Using Wavelet Transform," Journal of The Open Cybernetics & Systemics, Vol. 9, pp. 683-689, 2015.   DOI
2 D.L. Donoho, "Compressed Sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, pp. 1289-1306, 2006.   DOI
3 L. Gan, "Block Compressed Sensing of Natural Images," Proceedings of the International Conference on Digital Signal Processing, pp. 403-406, 2007.
4 S. Mun and J.E. Fowler, "Block Compressed Sensing of Images Using Directional Transforms," Proceedings of IEEE International Conference on Image Processing, pp. 3021-3024, 2009.
5 P.M. Pham, "Filter-Aided Recovery for Block- Based Compressive Sensing of Images," Proceedings of The 18th IEEE International Symposium on Consumer Electronics, pp. 22-25, 2014.
6 J. Zhang, D. Zhao, and F. Jiang "Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images," Proceedings of IEEE International Conference on Image Processing, pp. 1021-1025, 2013.
7 S. Mun and J.E. Fowler "Dpcm for Quantized Block-Based Compressed Sensing of Images," Proceedings of the European Signal Processing Conference, pp. 1424-1428, 2012.
8 C. Chen, E.W. Tramel, and J.E. Fowler, "Compressed Sensing Recovery of Images and Video Using Multihypothesis Predictions," Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, pp. 1193-1198, 2011.
9 K.Q. Dinh, H.J. Shim, and B. Jeon, "Measurement Coding For Compressive Imaging Using A Structural Measurement Matrix," Proceeding of the 20th International Conference on Image Processing, pp. 15-18, 2013.
10 B. Jeon, "Compressed Sensing and Image Processing Application," Proceedings of The Magazine of the The Institute of Electronics and Information Engineers, Vol. 41, No. 6, pp. 27-38, 2014.
11 Y. Park, H. Shin, and B. Jeon, "Convergence Complexity Reduction for Block-Based Compressive Sensing Reconstruction," Journal of The Korean Society of Broadcast Engineering, Vol. 19, No. 2, pp. 240-249, 2014.   DOI
12 M. Zhang, S. Kim, W.Y. Kim, and Y.H. Cho, "Formulation of Joint Decoding for Raptor Codes," Journal of Korea Multimedia Society, Vol. 17, No. 8, pp. 961-967, 2014.   DOI