An Improved Compressive Sensing Image Fusion Algorithm Based on NSCT Transform

Abstract:

A modified compressive sensing image fusion algorithm is proposed in this paper that is based on the NSCT transform. The algorithm is improved by introducing the theory of compressive sensing into image fusion that uses the NSCT transform to make a specific image be sparse on which only the high frequency coefficient is specifically measured; The improved algorithm then process the image fusion by retrieving the maximal value of the gradient of the neighborhood average from the measured high frequency coefficient, and accordingly, maximizing the absolute value of the neighborhood variance to the low-frequency counterpart. Afterwards, the improved algorithm can reconfigure the fusion image by using the MSP reconfiguration algorithm with final deliverable of the fusion image by committing to the NSCT reverse transform. Simulation results show that the improved algorithm is superior to other hand-on algorithms both in visual effect and in objective evaluation. In the case that the storage and transmission data are limited, the algorithm comes forth better effect of image fusion that is verified to be possesses of high value in practice.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 306-309
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.306
• Online since: October 2013
• Authors: Yan Hai Wu, Di Yan, Meng Xin Ma, Nan Wu
• Keywords: Compressive Sensing, Fusion Rule, Image Fusion, NSCT, Recovery Algorithm

References

[1] Do M N, Vetterli M. The contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091~2106.

DOI: 10.1109/tip.2005.859376

[2] Po D D, Do M N. Directional multiscale modeling of images using the contourlet transform[J]. IEEE Transactions on Image Processing, 2006, 15(6): 1610~1620.

DOI: 10.1109/tip.2006.873450

[3] Cunha A L, Zhou J, Do M N. The non-subsampled contourlet transform: Theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089~3101.

DOI: 10.1109/tip.2006.877507

[4] Wei D, Milenkovic O. Subspace Pursuit for Compressive Sensing Signal Reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230~2249.

DOI: 10.1109/tit.2009.2016006

[5] Wu Y H, Yan D. Compressive sensing image fusion based on modified SP algorithm[J]. Computer Application and Software, 2013, 30(7): 200~213.