BM-LRMR: Single Image Reconstruction Using Block-Matching and Low-Rank Matrix Recovery

Abstract:

In this letter, a method based on block-matching and Low-rank matrix recovery (BM-LRMR) is proposed for single image reconstruction. While existing approaches usually use many image sequences to restructure new images, our method tries to use a single image to realize the image reconstruction. In our algorithm, the single image is firstly partitioned into blocks. Then a block-matching technique is employed in grouping and constructing the approximately low-rank matrix. A low-rank matrix recovery is used in low-rank matrix decomposition. Finally, a weighted average strategy is using to compute a final estimate of the image by aggregating all of the obtained local estimates. Experimental results on different applications demonstrate the proposed model can work excellently for single image reconstruction.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 5125-5128
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.5125
• Online since: May 2014
• Authors: Bin Li*, De Lie Ming, Xiao Sun, Shui Ping Zhang, Hao Dou, Jin Wen Tian
• Keywords: Block Matching (BM), Low-Rank Matrix Recovery (LRMR), Single Image Reconstruction (BM-LRMR)

References

[1] L. Wang, L. Wang, M. Wen, Q. Zhuo, and W. Wang, Background subtraction using incremental subspace learning, in 2007 Proc. IEEE ICIP, vol. 5, pp.45-48.

DOI: 10.1109/icip.2007.4379761

[2] J. P. Costeira and T. Kanade, A multibody factorization method for independently moving objects, Int J. Comput. Vis., vol. 29, no. 3, pp.159-179, (1998).

[3] H. Krim and M. Viberg, Two decades of array signal processing research: the parametric approach, IEEE Signal Processing Magazine, vol. 13, no. 4, pp.67-94, (1996).

DOI: 10.1109/79.526899

[4] E. J. Candès, X. Li, Y. Ma, and J. Wright, Robust principal component analysis?, J. ACM, vol. 58, no. 3, pp.1-37, (2011).

[5] E. J. Candès and B. Recht, Exact matrix completion via convex optimization, Foundations of Computational Mathematics, vol. 9, no. 6, pp.717-772, (2009).

DOI: 10.1007/s10208-009-9045-5

[6] G. Liu, Z. Lin, and S. Yan, Robust recovery of subspace structures by low-rank representation, IEEE Trans. Pattern Anal. Mach., vol. 35, no. 1, pp.171-184, (2012).

DOI: 10.1109/tpami.2012.88

[7] 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, (2007).

DOI: 10.1109/tip.2007.901238

[8] Z. Lin, M. Chen, and Y. Ma, The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices, arXiv preprint, arXiv: 1009. 5055, (2010).

[9] A. Lopes, R. Touzi, and E. Nezry, Adaptive speckle filters and scene heterogeneity, IEEE Trans. Geosci. Remote Sens., vol. 28, no. 6, pp.992-1000, (1990).

DOI: 10.1109/36.62623

[10] F. Russo, A. D. Angelis, and P. Carbone, A vector approach to quality assessment of color images, in 2008 Proc. IEEE Instrumentation and Measurement Technology Conference, pp.814-818.

DOI: 10.1109/imtc.2008.4547149

[11] H. Xie, L. E. Pierce, and F. T. Ulaby, SAR speckle reduction using wavelet denoising and Markov random field modeling, IEEE Trans. Geosci. Remote Sens., vol. 40, no. 10, pp.2196-2212, (2002).

DOI: 10.1109/tgrs.2002.802473