Browse > Article
http://dx.doi.org/10.5573/ieie.2015.52.9.106

Visually Weighted Group-Sparsity Recovery for Compressed Sensing of Color Images with Edge-Preserving Filter  

Nguyen, Viet Anh (Sungkyunkwan University, College of Information & Communication Engineering)
Trinh, Chien Van (Sungkyunkwan University, College of Information & Communication Engineering)
Park, Younghyeon (Sungkyunkwan University, College of Information & Communication Engineering)
Jeon, Byeungwoo (Sungkyunkwan University, College of Information & Communication Engineering)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.9, 2015 , pp. 106-113 More about this Journal
Abstract
This paper integrates human visual system (HVS) characteristics into compressed sensing recovery of color images. The proposed visual weighting of each color channel in group-sparsity minimization not only pursues sparsity level of image but also reflects HVS characteristics well. Additionally, an edge-preserving filter is embedded in the scheme to remove noise while preserving edges of image so that quality of reconstructed image is further enhanced. Experimental results show that the average PSNR of the proposed method is 0.56 ~ 4dB higher than that of the state-of-the art group-sparsity minimization method. These results prove the excellence of the proposed method in both terms of objective and subjective qualities.
Keywords
Compressed sensing; Group-sparsity recovery; Color; Edge-Preserving Filter;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," Proc. of Int. Conf. Computer Vision, pp. 839-846, 1998.
2 E. Candes and J. Romberg, "Sparsity and incoherence in compressive sampling," Inverse Prob., vol. 23, pp. 969-985, 2007.   DOI   ScienceOn
3 Mohimani, M. Babaie-Zadeh, C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed l0norm," IEEE Trans. Signal Process., vol. 57, pp. 289-301, 2009.   DOI   ScienceOn
4 A. Nguyen, K. Q. Dinh, C. V. Trinh, and B. Jeon, "HVS based iterative hard thresholding recovery for compressive sensed image," Proc. of IEEE Int. Conf. Systems, Signal and Image Process. (IWSSIP), 2014.
5 A. Peterson, H. Peng, J. H. Morgan, and W. B. Pennebaker, "Quantization of color image components in the DCT domain," Proc. SPIE, Human Vision, Visual Process., vol. 1453, pp. 210-222, 1991.
6 L.Zang, D. Zang, X. Mou, D. Zang, "FSIM: A feature similarity index for image quality assessment," IEEE Trans. Image Process., vol. 20, pp. 2378-2386, 2011.   DOI   ScienceOn
7 H. T. Kung and S. J. Tarsa, "Partitioned compressive sensing with neighbor-weighted decoding," Proc. of Int. Conf. Military Comm. (MILCOM), pp. 149-156, 2011.
8 K. Q. Dinh, H. Shim, and B. Jeon, "Deblocking filter for artifact reduction in distributed compressed video sensing," Proc. of Visual Comm. Image Process. (VCIP), pp. 1-5, 2012.
9 D. L. Donoho, "Compressive sensing," IEEE Trans. Inform. Theory, vol. 52, pp. 1289-1306, 2006.   DOI   ScienceOn
10 S. Park, H. N. Lee, S. Park, "Introduction to Compressed Sensing," The Magazine of the IEEK, vol. 38, pp. 19-30, 2011.
11 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.
12 A. Majumdar and R. K. Ward, "Compressed sensing of color images," Jour. Signal Process., vol. 90, pp. 3122-3127, 2010   DOI   ScienceOn
13 P. Nagesh and B. Li, "Compressive imaging of color images," Proc. of IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), pp. 1261-1264, 2009.
14 V. A. Nguyen, K. Q. Dinh, C. V. Trinh, and B. Jeon, "Smoothed group-sparsity iterative hard thresholding recovery for compressive sensing of color image," Journal of the Institute of Electronics and Information Engineers, vol. 51, pp. 849-856, 2014.