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Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
Novel image fusion techniques using global and local kekre wavelet transforms
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Novel image fusion techniques using global and local kekre wavelet transforms

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  • 1. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET) & TECHNOLOGY January- February (2013), © IAEMEISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 1, January- February (2013), pp. 89-96 IJCET© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEMEwww.jifactor.com NOVEL IMAGE FUSION TECHNIQUES USING GLOBAL AND LOCAL KEKRE WAVELET TRANSFORMS Dr. Sudeep D. Thepade Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune Mrs. Jyoti S.Kulkarni Senior Lecturer,Department of Information Tech., Pimpri Chinchwad College of Engineering, Pune ABSTRACT Image Fusion is the process of combining the information from multiple images such that the fused image gives or represents more information than that of single image gives. Images for image fusion may be from single sensor with different time slots or may be from multiple sensors. Image fusion is used in different applications such as medical imaging, Military images, Multisensory images, Multifocus images etc. Different transforms are used for image fusion. In this proposed method, Kekre transform is used. Kekre transform is one of the orthogonal transforms. Here Kekre transform along with Local Kekre Wavelet Transform and Global Kekre Wavelet Transform are proposed to be used in novel image fusion methods. For each of the proposed Image Fusion techniques , the Average , Minimum and Maximum are considered for generation of fused image. Experimentation is performed on ten sets of images to generate the fused images. Result has shown the Local Kekre wavelet transform proves to be better for image fusion than Global Kekre wavelet transform and Kekre Transform. Also the averaging based fusion is better than minimum or maximum. Keywords- Kekre Transform, Local Transform, Global Transform 89
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEI. INTRODUCTION Now a days, Image fusion is used in many fields. In this, the images from differentsensors or from same sensor with different time or season are fused together. After imagefusion, the resultant image generated will be more informative than source images. In fusedimage, the relevant information from source images get used to find more information whichwill be further processed. There are increasing applications of image fusion in different fields. In topographicmapping, the area that is not covered by one sensor might be available in another sensor. Bycombining the information from these sensors, the area or map is updated. Similarly to getthe idea about natural hazards such as flood monitoring and snow monitoring. Image fusion isalso used in geology to get the information on soil geochemistry, vegetation, land use, soilmoisture and surface roughness. Many image fusion methods are available such as Intensity Hue Saturation (IHS),Principle Component Analysis (PCA), Brovey Transform (BT), High Pass Filtering (HPF) ,High Pass Modulation (HPM) and Transform domain image fusion.. Here use of Kekre Transform is proposed along with Local Kekre Wavelet Transformand Global Kekre Wavelet Transform for Image Fusion. Section II describes the Kekre Transform and generation of Local Kekre WaveletTransform and Global Kekre Wavelet Transform. Section III describes the proposed ImageFusion Method with block diagram. Section IV describes the experimentation on proposedImage Fusion Method and Section V describes the results and discussion on the fused imagesby using these methods.II. USED TRANSFORMS(a) Kekre Transform: In Kekre Transform, it is not essential that the matrices have to be inpowers of 2. The Kekre Transform is generated by using equation (1). 1 , x≤y { Kx,y = (-N+(x+1) , x=y+1 0 , x>y+1 (1) Let the following matrix generated by this equation having PxP size. 1 2 … P 1 … 2 … ⁞ ⁞ ⁞ … ⁞ ⁞ P … Figure 1: PxP Kekre Transform matrix 90
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME(b) Local Kekre WaveletTransform : Generation of Local Kekre Wavelet Transform of sizeP2xP2 from Kekre Transform of size PxP is shown in fig. 2. … … … … … … … … ⁞ ⁞ … ⁞ ⁞ ⁞ … ⁞ … ⁞ ⁞ … ⁞ … … … … … 0 0 … 0 … 0 0 … 0 0 0 … 0 … … 0 0 … 0 ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ 0 0 … 0 0 0 … 0 … … … … … … … 0 0 … 0 … 0 0 … 0 0 0 … 0 … … 0 0 … 0 ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ 0 0 … 0 0 0 … 0 … … Figure 2: P2xP2 matrix of Kekre Local Wavelet Transform(c) Global Kekre Wavelet Transform: Generation of Global Kekre Wavelet Transform of sizeP2xP2 from Kekre Transform of size PxP is shown in fig. 3. … … … … … … … … ⁞ ⁞ … ⁞ ⁞ ⁞ … ⁞ … ⁞ ⁞ … ⁞ … … … … … 0 0 0 0 0 0 0 0 0 ⁞ ⁞ … ⁞ 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ⁞ ⁞ … ⁞ 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 0 0 0 0 ⁞ 0 0 0 0 0 0 0 0 0 0 0 0 0 … 0 0 0 0 0 0 0 0 0 ⁞ ⁞ … ⁞ 0 0 0 0 0 0 0 0 0 … Figure 3: P2xP2 matrix of Kekre Global Wavelet TransformThe normalization of these wavelet transforms done before the use in processing. 91
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEIII. PROPOSED IMAGE FUSION METHOD The proposed image fusion method using Kekre wavelet transform is given in fig.4. Apply Local / Transformed Image 1 Global Kekre Fusion using Image Transform Minimum/M Inverse Fused aximum/Ave Transform Image rage Apply Local / Method Image 2 Global Kekre Transformed Transform Image Figure 4: Basic Block Diagram of proposed Image Fusion MethodIn the proposed Image Fusion Method, normalized local or global transform is applied to twoblurred images separately. Three coefficients are available after this transformation. Theinverse transform is applied to find fused image. The coefficients are compared to find betterfused image.IV. IMPLEMENTATION / EXPERIMENTATION For experimentation, set of six images are considered to fused using proposed imagefusion method as shown in fig 5. Set1: Tulip Set2: Moon Set3: Animal Set4: Apple Set5: Wool Set6: Scene Figure 5: Test bed of set of images to be fused 92
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEV. RESULTS AND DISCUSSION The Kekre Transform, Local Kekre Wavelet Transform and Global Kekre WaveletTransform is applied on the images given in fig.5. Then the fused image is compared withoriginal image to find the mean square error. Result generated shows that local transform isbetter than orthogonal transform as well as global transform.Table1. Comparison of Kekre Transform, Kekre Local Wavelet transform and Kekre GlobalTransform Kekre Wavelet Kekre Wavelet Kekre Local Global Transform transform Transform Tulip 271.7474 275.9028 17176 Moon 91.3706 91.1495 24568 Animal 122.5144 122.5086 13581 Apple 363.9339 363.9184 18797 Wool 385.4935 397.9610 15315 Scene 191.2848 190.5862 15438 Average 237.72 240.34 17479.17 Comparison of KT, LKWT,GKWT 30000 25000 Kekre transform MSE VAlue 20000 15000 Kekre Wavelet 10000 Global Transform 5000 Kekre Wavelet 0 Local Transform Figure 6: Graphical representation of comparison between transforms. 93
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME By considering the Kekre Local wavelet Transform, the minimum, maximum andaverage values are compared. After comparison it shows that average value is better thanother two values.Table2. Comparison of Average, Minimum and Maximum values in Local KekreWavelettransform Average Minimum Maximum Tulip 271.7474 326.2178 327.652 Moon 91.3706 102.0376 105.223 Animal 122.5144 143.7189 148.514 Apple 363.9339 413.4674 440.807 Wool 385.4935 474.4746 479.406 Scene 191.2848 217.3164 230.101 500 400 MSE value 300 Average 200 Minimum Maximum 100 0 Tulip Moon Animal Apple Wool Scene Images Figure 7: Graphical representation of comparison between values of Kekre Local Wavelet transforms. 94
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEThe output images after the application of these three transforms is given in figure 8. (a)Original (b) Blurred (c) Blurred Image Image Image (d) Average (e) (f) Maximum Minimum Kekre Local Wavelet Transform (g) Average (h) Minimum (i) Maximum Kekre Transform (j) Average (k) Minimum (l) Maximum Kekre Global Wavelet Transform Figure 8: Input and Output Images of all the Transforms.Figure 8 represents the input and output images of Kekre Transform, Kekre Local wavelettransform and Kekre Global Wavelet Transform. In figure, first row are the original imageand blurred images respectively. Second row represents outputs of Kekre Local WaveletTransform with Minimum, Maximum and Average values respectively. Third row representsoutputs of Kekre Transform with Minimum, Maximum and Average values respectively. Andlast row represents, outputs of Kekre Global Wavelet Transform with Minimum, Maximumand Average values respectively. From this, the Kekre Local Wavelet transform gives betterresult than remaining two methods. 95
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMECONCLUSIONS Image Transform based fusion is gaining momentum in imaging research. Here novelImage Fusion methods are proposed using Kekre Transform, Local Kekre Wavelet Transformand Global Kekre Wavelet Transform .Experimentation has shown that the Kekre WaveletTransform based fusion is outperforming. In all Local Kekre Wavelet Transform withaveraging gives better performance for image fusion.REFERENCES[1]H.B.Kekre, Tanuja K. Sarode, Sudeep D.Thepade, Sonal Shroff,”Instigation of OrthogonalWavelet Transforms using Walsh, Cosine, Hartley, Kekre Transforms and their use in ImageCompression”,International Journal of Computer Science and Information Security, Vol.9,No.6,2011,125-133.[2]H.B.Kekre, Archana Athawale, Dipali Sadavarti,”Algorithm to generate Kekre’s WaveletTransform from Kekre’s Transform”,International Journal of Engineering Science andTechnology, Vol.2(5),2010,756-767.[3] H.B.Kekre, Tanuja K. Sarode, Sudeep D.Thepade, ,”Inception of Hybrid WaveletTransform using two Orthogonal Transforms and it’s use for ImageCompression”,International Journal of Computer Science and Information Security, Vol.9,No.6,2011,80-87.[4] Peijun Du,Sicong Liu, Junshi Xia, Yindi Zhao,”Information Fusion Techniques forchange detection from multi temporal remote sensing images”[5]Yufeng Zheng,Edward A Essock,”Alocal coloring method for night vision colorizationutilizing image analysis and fusion”, Information Fusion 9(2008) 186-199.[6]Chandan Singh, Pooja,”An effective image retrieval using the fusion of global and localtransforms based features”, Optics and Laser Technology 44(2012), 2249-2259.[7]Yong Yang, “A Novel DWT based multi focus image fusion method”, Procediaengineering 24(2011) 177- 181.[8]Youdong Ding, Cai Xi, Xiaocheng Wei, Jianfei Zhang,”A new framework for imagecompletion based on image fusion technology”, Procedia engineering 29(2012) 3826-3830.[9]Tao Wu, Xiao-Jun Wu, Xiao-Qing Luo,”A study on fusion of different resolution images”,Procedia engineering 29(2012) 3980-3985.[10] Deng Minghui, Zeng Qingshung, Zhang Lanying,”Research on Fusion of Infrared andvisible images based on directionlet transform”, IERI procedia 3(2012) 67-72.[11] B.V. Santhosh Krishna, AL.Vallikannu, Punithavathy Mohan and E.S.Karthik Kumar,“Satellite Image Classification Using Wavelet Transform” International journal ofElectronics and Communication Engineering &Technology (IJECET), Volume 1, Issue 1,2010, pp. 117 - 124, Published by IAEME.[12] B.K.N.Srinivasa Rao and P.Sowmya, “Architectural Implementation Of VideoCompression Through Wavelet Transform Coding And Ezw Coding” International journal ofElectronics and Communication Engineering &Technology (IJECET), Volume 3, Issue 3,2012, pp. 202 - 210, Published by IAEME.[13] Hitashi and Sugandha Sharma, “Fractal Image Compression Scheme UsingBiogeography Based Optimization On Color Images”, International journal of ComputerEngineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 35 - 46, Published byIAEME. 96

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