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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,123-1,141, DOI: https://doi.org/10.21307/ijssis-2017-799
License : (CC BY-NC-ND 4.0)
Received Date : 30-January-2015 / Accepted: 12-April-2015 / Published Online: 01-June-2015
Due to the different imaging mechanism of optical image and Synthetic Aperture Radar (SAR) image, they have the large different characteristics between the images, so fusing optical image and SAR image with image fusion technology could complement advantages and be able to better interpret the scenes information. A fusion algorithm of Synthetic Aperture Radar and optical image with fast sparse representation on low-frequency images was proposed. For the disadvantage of target information easily missing and the contrast low in fused image, and the fusion method with sparse representation could effectively retain target information of Synthetic Aperture Radar image, so the
paper fuses low frequency images of Synthetic Aperture Radar and optical images using sparse representation. Moreover a new sparse coefficient fusion rules is proposed, and sparse decomposition process is improved to reduce the algorithm running time. Experimental results demonstrate the effectiveness of the algorithm.
 Wang Wen-cheng, Chang Fa-liang. A Multi-focus Image Fusion Method Based on Laplacian
Pyramid, Journal of Computers, 2011, 6 (11): 2559-2566.
 A. Baradarani, Jonathan Q M, M. Ahmadi, et al. Tunable half band-pair wavelet filter banks
and application to multi-focus image fusion, Pattern Recognition, 2012, 45 (2): 657-671.
 Sale, D.; Patil, V. ; Joshi, M.A, Effective image enhancement using hybrid multi resolution
image fusion, 2014 IEEE Global Conference on Wireless Computing and Networking
(GCWCN), pp.116 - 120, 2014.
 Wei Huan-ga, Jing Zhong-liang, Evaluation of focus measures in multi-focus image fusion,
Pattern Recognition Letters, 2007, 28 (4): 493-500.
 Iuliia Shatokhina, Andreas Obereder, Matthias Rosensteiner, et al, Preprocessed cumulative
reconstructor with domain decomposition: a fast wave-front reconstruction method for
pyramid wave-front sensor [J]. Applied Optics, 2013, 52 (12): 2640-2652.
 Aritra Sengupta, Noel Cressie, Hierarchical statistical modeling of big spatial datasets using
the exponential family of distributions [J]. Spatial Statistics, 2013, 4 (1): 14-44.
 Harikumar, V. et al., Multi-resolution 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.
 Upla, K.P. ; Joshi, M.V. ; Gajjar, P.P., An Edge Preserving Multiresolution Fusion: Use of
Contourlet Transform and MRF Prior, IEEE Transactions on Geoscience and Remote
Sensing, vol.53, no.6, pp.3210 - 3220, 2015.
 Min Li, Gang Li, Wei Cai, Xiao-yan Li. A Novel Pixel-Level and Feature-Level Combined
Multisensor Image Fusion Scheme, Lect. Notes in Computer Science, 2008, 52 (1): 658-665.
 Benjamin W. Martin, Ranga R. Vatsavai. Evaluating fusion techniques for multi-sensor
satellite image data, Proc. of SPIE Vol, 2013 (8747), 87470J.
 Xing Su-xia, Lian Xiao-feng,Chen Tian-hua, et al, Image Fusion Method Based on NSCT
and Robustness Analysis, 2011 International Conference on Computer Distributed Control
and Intelligent Environmental Monitoring, 2011: 346-349.
 Bin Yang, Shutao Li, Pixel-level image fusion with simultaneous orthogonal matching
pursuit, Information Fusion,2012,13(1):10-19.
 Zhou Wang, Bovik AC, A Universal Image Quality Index, IEEE Signal Processing
Letters, 2002, 9 (3): 81-84.
 Piella G, Heijmans H. Anew quality metric for image fusion[C]. The International
Conference on Image Processing, 2003, 3: 173-176.
 Shuang Li, Zhilin Li, Jianya Gong, Multivariate statistical analysis of measures for
assessing the quality of image fusion[J]. International Journal of Image and Data Fusion,
2010, 1 (1): 47-66.
 Susanta Mukhopadhyay, Bhabatosh Chanda, Fusion of 2D gray-scale images using multiscale
Morphology[J]. Pattern Recognition, 2001, 34 (10): 1939-1949.
 Xiangzhi Bai. Image fusion through feature extraction by using sequentially combined
toggle and Top-Hat based contrast operator [J]. Applied Optics, 2012, 51 (31): 7566-7575.
 Yang B. and Li S. T. Pixel-level image fusion with simultaneous orthogonal matching
pursuit, Inf. Fusion. 2012, 13 (1): 10-19.
 Jun Wang, Jinye Peng, Xiaoyi Feng, et al. Image fusion with non-subsampled contourlet
transform and sparse representation, Journal of Electronic Imaging, 2013, 22 (4): 1-15.
 Aharon M, Elad M, Bruckstein AM, The K-SVD: An algorithm for designing of overcomplete
dictionaries for sparse representations, IEEE Tran. Image Process, 2006, 54 (11):
 Yang J Y, Peng Y G, Xu W L, et al. Ways to sparse representation: an overview, Science in
China Series F: Information Sciences, 2009, 52 (4): 695-703.
 Yang B, Li S T. Multi-focus Image Fusion and Restoration with Sparse Representation,
IEEE Transactions on Instrumentation and Measurement, 2010, 59 (4): 884-892.
 Liqiang Guo, Ming Dai, Ming Zhu, Multi-focus color image fusion based on quaternion
curvelet transform, 2012, 20 (17): 18846-18860.
 Erik Reinhard , Michael Ashikhmin, Bruce Gooch , Peter Shirley. Color Transfer between
images, IEEE Computer Graphics and Applications, 2001, 21 (5): 34-41.
 Takashi Kondo, Xiaohua Zhang, Coloring of Gray Scale Image Using Image Color
Transfer, The Journal of The Institute of Image Information and Television Engineers, 2007,
61 (6): 838-841.
 Toet A, Franken E M. Perceptual evaluation of different image fusion schemes [J].
Displays, 2003, 24 (1): 25-37.
 David Blacknell ; Nicholas S. Arini ; Ian McConnell, SAR image understanding using
contextual information, Proc. SPIE 4543, SAR Image Analysis, Modeling, and Techniques
IV, vol. 4543, pp. 73-84,2002.
 Xixi Huang, Xiaofeng Wang, The Classification of Synthetic Aperture Radar Oil Spill
Images Based on the Texture Features and Deep Belief Network, Lecture Notes in Electrical
Engineering, vol.277, pp 661-669, 2014.
 N. K. Suryadevara, S. C. Mukhopadhyay. R.K. Rayudu and Y. M. Huang, Sensor Data
Fusion to determine Wellness of an Elderly in Intelligent Home Monitoring Environment,
Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN
978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 947-952.
 Yueting Zhang, Chibiao Ding, Hongzhen Chen, Hongqi Wang, Special Phenomena of the
Shadow Region in the High Resolution Synthetic Aperture Radar Image due to Synthetic
Aperture, Journal of Infrared, Millimeter, and Terahertz Waves, Volume 33, Issue 10, pp
 Wei Liang, S.C. Mukhopadhyay, Rajali Jidin and Chia-Pang Chen, Multi-Source
Information Fusion for Drowsy Driving Detection Based on Wireless Sensor Networks,
Proceedings of the 2013 7th International Conference on Sensing Technology, ICST 2013,
December 3-5, 2013, Wellington, New Zealand, pp. 861-868, ISBN 978-1-4673-5221-5.
 Wisnu Jatmiko, Ikhsanul Habibie, et al., Automated telehealth system for fetal growth
detection and approximation of ultrasound images, International Journal on Smart Sensing
and Intelligent Systems, 8(1), pp.697 – 719, 2015.
 S. Bhardwaj, D. S. Lee, S.C. Mukhopadhyay, and W. Y. Chung, A Fusion Data
Monitoring of Multiple Wireless Sensors for Ubiquitous Healthcare System, Proceedings of
the 2nd International Conference on Sensing Technology Nov. 26-28, 2007 Palmerston North,
New Zealand, pp. 217-222.
 Chastine Fatichah, Diana Purwitasari, et al., Overlapping white blood cell segmentation
and counting on microscopic blood cell images, International Journal on Smart Sensing and
Intelligent Systems, 7(4), pp. 1271 – 1286, 2014.