Total views : 307

Fusion of SONAR Image using Enhanced Multi-Scale Transform and Sparse Representation Method


  • Research Department of Computer Science, D.G.Vaishnav College, Arumbakkam, Chennai-600106, Tamil Nadu, India


Objective: The main goal of this paper is to enhance fusion of sonar image thereby achieve the better entropy, standard deviation and PSNR value. Methods: Multi-Scale Transforms (MST) and Sparse Representation (SR) methods are the two well-known methods used in image and signal representation theory. The novel image fusion framework is proposed in this paper by the combining enhanced MST method and SR based image fusion. The proposed scheme consists of three phase; first de-noised the sonar image using DTCWT with mean filter; second select the obtained pixels or features from sonar image using Novel PCA method; third obtained fusion image using Enhanced MST with SR structure. Findings: It is good at suppressing noise, especially for images with a higher noise level. The advantage of the proposed enhanced MST with SR technique than conventional MST with SR method is different level of decomposition using four popular MST methods; DWT, DTCWT, CVT and NSCT. The proposed method obtained better result in terms of entropy, standard deviation compared to conventional method. Applications: To realize earth surfaces with focus on underwater applications like depth sounding, sea-bed imaging and fish echolocation the SOund Navigation And Ranging (SONAR) technology is used.


Denoising, Image Fusion, Multi-Scale Transforms, Sparse Representation, Sonar Image.

Full Text:

 |  (PDF views: 175)


  • Lingjie M, Chujiang L, Zengbin W, Zhiqiang S. Development and military applications of multi-source image fusion technology. Aerospace Electronic Warfare. 2011; 3.
  • Toet T.Image fusions by a ratio of low pass pyramid. Pattern Recognition Letters. 1989; 9(4):245–53.
  • Burt P, Adelson E. The laplacian pyramid as a compact image code. IEEE Transactions on Communications. 1983; 31(4):532–40.
  • Beaulieu M, Foucher S, Gagnon L. Multi-spectral image resolution refinement using stationary wavelet transform.International Geoscience and Remote Sensing Symposium.2003 Jul 21; 6:VI-4032.
  • Petrovic V, Xydeas C. Gradient-based multire solution image fusion. IEEE Transactions on Image Processing.2004; 13(2):228–37.
  • Li H, Manjunath B, Mitra S. Multisensor image fusion using the wavelet transform, Graph. Models Image Process. 1995; 57(3):235–45.
  • Zhang Q, Guo B. Multifocus image fusion using the nonsub sampled contourlet transform. Signal Process. 2009; 89(7):1334–46.
  • Lewis J, OCallaghan R, Nikolov S, Bull D, Canagarajah N.Pixel- and region based image fusion with complex wavelets. Information Fusion. 2007; 8(2):119–30
  • Nencini F, Garzelli A, Baronti S, Alparone L. Remote sensing image fusion using the curvelet transform. Information Fusion. 2007; 8(2):143–56.
  • MynaAN, Prakash J. A novel hybrid approach for multifocus image fusion using fuzzy logic and wavelets.International Journal of Emerging Trends and Technology in Computer Science. 2014; 3(2):1–8.
  • Mandhare RA, Upadhyay P, Gupta S. Pixel-level image fusion using Brovey Transforme and wavelet transform.International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2013 Jun; 2(6):2690–5.
  • Yang B, Li S.Multifocus image fusion and restoration with sparse representation. IEEE Transactions on Instrumentation and Measurement. 2010; 59(4):884–92.
  • Yu N, Qiu T, Bi F, Wang A. Image features extraction and fusion based on joint sparse representation. IEEE Journal of Selected Topics in Signal Processing. 2011; 5(5):1074–82
  • Liu Y, Wang Z. Multi-focus image fusion based on sparse representation with adaptive sparse domain selection.Proceedings of 7th International Conference on Image and Graphics, China; 2013. p. 591–6.
  • Yin H, Li S, Fang L. Simultaneous image fusion and superresolution using sparse representation. Information Fusion.2013; 14(3):229–40.
  • Ellmauthaler A. Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks. IEEE Transactions on Image Processing.2013 Mar; 22(3):1005–17.
  • Pradnya PM, Ruikar SD. Image Fusion Based On Stationary Wavelet Transform. International Journal of Advanced Engineering Research and Studies. 2013 Jul–Sep:99–101.
  • Purushotham A, Rani GU, Naik S. Image fusion using DWT and PCA. International Journal of Advanced Research in Computer Science and Software Engineering.2015; 5(4):800–4.
  • Pajares G, Cruz J. A wavelet-based image fusion tutorial.Pattern Recognition. 2004; 37(9):1855–72.
  • Li S, Yang B, Hu J. Performance comparison of Different multi-resolution Transforms for image fusion. Information Fusion, Elsevier, .Article in press. DOI 10.1016/j.inffus.2010.03.002.
  • Kingsbury NG. The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters.8th IEEE DSP Workshop, Bryce Canyon, August UK; 1998.p. 1–4.
  • Kingsbury NG. The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement.EUSIPCO 98, Rhodes, UK; 1998.
  • TurkM, Pentland A. Eigen faces for recognition. Journal of Cognitive Neuroscience. 1991; 3(1):71–86
  • Haykin S.Neural networks a comprehensive foundation, (2nd ed.) Prentice-Hall. 1999, 13(4):409–12.
  • Mallat ZS, Zhang Z. Matching pursuits with time-frequency dictionaries.IEEE Transactions on Signal Processing. 1993; 41(12):3397–415.
  • IJ Hasan, CK Gan, M Shamshiri, MR Ab Ghani, RB Omar.Optimum Feeder Routing and Distribution Substation Placement and Sizing Using PSO and MST. Indian Journal of Science and Technology. 2014 Jan;7 (10):1682–9, DOI: no:10.17485/ijst/2014/v7i10/50276.
  • Sindhubargavi R, Sindhu MY, Saravanan R. Spectrum sensing using energy detection technique for cognitive radio networks using PCA technique. Indian Journal of Science and Technology. 2014 Apr; 7(S4).DOI:10.17485/ijst/2014/ v7i4/50048.
  • Pranab garg and Dinesh Kumar. Image Pattern Extraction and Compression using Pixel Neighborhood and Weighted PCA Algorithm. Indian Journal of Science and Technology.2016; 9(28), DOI: 10.17485/ijst/2016/v9i28/95468.
  • AanchalJhingan and Lavish Kansal. Performance Analysis of FFT-OFDM and DWT-OFDMover AWGN Channel under the Effect of CFO. Indian Journal of Science and Technology. 2016 Feb; 9(6), DOI: 10.17485/ijst/2016/ v9i6/80047.
  • Memon AP, Uqaili MA, Memon ZA, Ali AA, Zafar A.Combined novel approach of DWT and feedforward MLPRBF network for the classification of power signal waveform distortion. Indian Journal of Science and Technology. 2014 Jan; 7(5).DOI:10.17485/ijst/2014/v7i5/50158.
  • K. P. Indira and R. Rani Hemamalini. Evaluation of Choose Max and Contrast based Fusion Rule Using DWT for PET, CT Images. Indian Journal of Science and Technology. 2015 July; 8(15). DOI: 10.17485/ijst/2015/v8i15/74556 .


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.