Energy Efficient Compressive Sensing based Multi-focus Image Fusion Scheme for WVSN
DOI:
https://doi.org/10.26438/ijcse/v6i11.274280Keywords:
WVSN, multi-focus image fusion, energy efficiencyAbstract
Wireless Visual Sensor Networks (WVSN) is the enhanced version of WSN that captures and processes visual data from the environment. Owing to the numerous advantages, the WVSN is widely applicable in several real-time applications. The main considerations of WVSN are energy efficiency and quality. This work intends to propose an energy efficient compressive sensing based multi-focus image fusion scheme for WVSN. The image fusion is carried out by contourlet and curvelet, as these have multi-scale and multi-directional properties. The image fusion rule is framed by considering the energy of the pixels. Finally, the image reconstruction is carried out by CoSaMP (Compressive Sampling Matching Pursuit) algorithm. The performance of the proposed approach is tested in terms of image quality, energy and time consumption and the results are compared with the existing approaches. The proposed image fusion scheme outperforms the existing approaches, while proving better quality.
References
[1] J. L. Starck, E.J. Candes, D.L. Donoho, “The curvelet transform for image denoising”, IEEE Transactions on Image Processing, Vol.11, No.6, pp.670-684, 2002.
[2] M.N. Do, M.Vetterli, "The contourlet transform: an efficient directional multi-resolution image representation", IEEE Transactions on Image Processing, Vol.14, No.12, pp. 2091-2106, 2005.
[3] G. Easley, D.Labate,W.Q.Lim, "Sparse directional image representations using the discrete shearlet transform, Appl.Comput.Harmon.Anal, Vol.25, No.1, pp.25-46, 2008.
[4] A.Khare, R.Srivastava, R.Singh, "Edge preserving image fusion based on contourlet transform,in: Proceedings of the 5th International Conference on Image and Signal Processing, Agadir, Morocco, pp.93–102, 2012.
[5] Atul Divekar ; Okan Ersoy, "Image fusion by compressive sensing", 17th International Conference on Geoinformatics, Fairfax, USA, 12-14 Aug, 2009.
[6] Morteza Ghahremani ; Hassan Ghassemian, "Remote Sensing Image Fusion Using Ripplet Transform and Compressed Sensing", IEEE Geoscience and Remote Sensing Letters, Vol.12, No.3, pp. 502-506, 2015.
[7] Jingbo Wei ; Lizhe Wang ; Peng Liu ; Xiaodao Chen ; Wei Li ; Albert Y. Zomaya, "Spatiotemporal Fusion of MODIS and Landsat-7 Reflectance Images via Compressed Sensing", IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No.12, pp.7126-7139, 2017.
[8] Zhen-Zhen Yang ; Zhen Yang, "Novel multifocus image fusion and reconstruction framework based on compressed sensing", IET Image Processing, Vol.7, No.9, pp. 837-847, 2013.
[9] V. Harikumar ; Prakash P. Gajjar ; Manjunath V. Joshi ; Mehul S. Raval, "Multiresolution Image Fusion: Use of Compressive Sensing and Graph Cuts", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.7, No.5, pp. 1771-1780, 2014.
[10] Fuzhen Zhu ; Hongchang He ; Xiaofei Wang ; Qun Ding, "A New Multi-spectral Image Fusion Algorithm Based on Compressive Sensing", Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control, Qinhuangdao, China, 18-20 Sep, 2015.
[11] Vahdat Kazemi ; Hadi Seyedarabi ; Ali Aghagolzadeh, "Multifocus image fusion based on compressive sensing for visual sensor networks", 22nd Iranian Conference on Electrical Engineering, Tehran, Iran, 20-22 May, 2014.
[12] Tao Wan ; Nishan Canagarajah ; Alin Achim, "Compressive image fusion", 15th IEEE International Conference on Image Processing, 12-15 Oct, San Diego, USA, 2008.
[13] B. Sathyabama ; S.G. Siva Sankari ; S. Nayagara, "Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method", Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 18-21 Dec, Jodhpur, India, 2013.
[14] Yang Senlin ; Li Yuanyuan ; Wan Guobin, "Remote-sensing images fusion by compressed sensing in contourlet transform domain", IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), Ottawa, ON, Canada, 29-30 Sep, 2014.
[15] Xu Wei ; Wen Jianguo ; Chen Yinzhu, "Fusion of Remote Sensing Image with Compressed Sensing Based on Wavelet Sparse Basis", Sixth International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, 10-11 Jan, 2014.
[16] Ye Zhang ; Jian Zhang, "The Image Fusion of Compressive Sensing with Adaptive Deviation Feature", International Conference on Intelligent Transportation, Big Data & Smart City, Xiamen, China, 25-26 Jan, 2018.
[17] P.G.Jaywantrao, S.Hasan, "Application of image fusion using wavelet transform in target tracking system", Int.J.Eng.Res.Technol., Vol.1, No.8, pp. 1-6, 2012.
[18] S.R. Bijitha, L.Mohan, M.M.Kartha, A.P.Kurian, K.P.Soman, "Modified wavelet image fusion based on SVD", Int.J.Eng.Res.Technol., Vol.1, No.9, pp. 1-8, 2012.
[19] L.Y.Du,Y.Jie,Z.Xu, "A new adaptive image fusion technique of CT and MRI images based on dual-tree complex wavelet transform", Appl.Mech.Mater., Vol. 411–414, pp.1189–1192, 2013.
[20] Do, Minh N., and Martin Vetterli. "The contourlet transform: an efficient directional multiresolution image representation." IEEE Transactions on image processing 14.12 (2005): 2091-2106.
[21] Subbulakshmi T.C., Dr. Gnanadurai, D., Dr. Muthulakshmi I., “Energy Conserving Forepart Detection Scheme with Dynamic Compressive Measurements based on Compressive Sensing for WVSN”, Journal of Internet Technology, Accepted, 2018.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
