Research of Adaptive Gradient Projection Algorithm on Remote Sensing Image Reconstruction

Abstract:

In order to improve the low efficient and the noise effect of remote sensing image reconstruction, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. Point to the high frequency noise of remote sensing image, the ADGPSR algorithm pursuits the projection direction in two conjudate directions, thus the high frequency noise effect is overcame. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm on remote sensing image improves the signals reconstruction accuracy.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 572-575
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.572
• Online since: September 2013
• Authors: Hai Xia Yan, Yan Jun Liu
• Keywords: Compressed Sensing, Gradient Projection, Remote Sensing Image, Signal Processing

References

[1] D. Donoho, Compressed sensing[J]. IEEE Trans. Inform. Theory. 2006, 52 (4): 1289–1306.

DOI: 10.1109/tit.2006.871582

[2] Candès E.J., Romberg,J., Tao,T., Signal recovery from incomplete and inaccurate measurements[J]. Comm. Pure. Appl. Math. 2005, 59(8): 1207-223.

DOI: 10.1002/cpa.20124

[3] Donoho D, Huo X, Uncertainty Principles and Ideal Atomic Decompositions[J]. IEEE Trans Inform Theory, 2001, 47(7): 2845-2862.

DOI: 10.1109/18.959265

[4] Lei, J. Generalized reconstruction algorithm for compressed sensing[J]. Computers and Electrical Engineering, v 37, n 4, pp.570-588, July (2011).

DOI: 10.1016/j.compeleceng.2011.04.007

[5] M. Figueiredo,R. Nowak,S. Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J], IEEE Journal of Slaected Topics in Signal Processing, 2007. 12, 1(4): 586-597.

DOI: 10.1109/jstsp.2007.910281

[6] Satyanarayana, J.V. Ramakrishnan, A.G. An efficient document image reconstruction and binarization method using Compressed Sensing[J]. Proceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2009, CGVCVIP 2009. Part of the IADIS MCCSIS 2009, pp.287-291, 2009, Proceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2009, CGVCVIP 2009. Part of the IADIS MCCSIS (2009).

DOI: 10.1109/cgiv.2009.89

[7] E. Candès,J. Romberg,T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J], IEEE Trans. Inform. Theory, 2006, 52(2): 489-509.

DOI: 10.1109/tit.2005.862083

[8] Egiazarian, Karen. Compressed sensing image reconstruction via recursive spatially adaptive filtering[J]. Proceedings - International Conference on Image Processing, ICIP, v 1, p I549-I552, 2006, 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings.

DOI: 10.1109/icip.2007.4379013

[9] Ni Kangyu, Datta Somantika, Mahanti Prasun. etc. Using reed-muller sequences as deterministic compressed sensing matrices for image reconstruction[J]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp.465-468.

DOI: 10.1109/icassp.2010.5495714

[10] Le Montagner, Yoann, Marim Marcio. etc. Numerical evaluation of sampling bounds for near-optimal reconstruction in compressed sensing[J]. Proceedings - International Conference on Image Processing, ICIP, pp.3073-3076.

DOI: 10.1109/icip.2011.6116314

[11] Mun, Sungkwang. Residual reconstruction for block-based compressed sensing of video[J]. Data Compression Conference Proceedings, pp.183-192, 2011, Proceedings - DCC 2011: 2011 Data Compression Conference.

DOI: 10.1109/dcc.2011.25