17. Optimal Image Fusion using Neuro-Fuzzy Algorithm and SVM

30 | P age Australian Journal of Information Technology and Communication Volume II Issue I ISSN 2203-2843 Optimal Image Fusion using Neuro-Fuzzy Algorithm and SVM Ms Maninder Kaur1, Ms Pooja2 (Department of CSE,CTIEMT,CT Institution Shahpur,Jalandhar) 1 maninder.khinda43@gmail.com (Department of CSE, CTIEMT, CT Institution Shahpur,Jalandhar) 2 poojachoudhary80@gmail.com Abstract: This thesis presents a novel image fusion method which is suitable for pan-sharpening of multispectral (MS) bands, based on multi-resolution analysis. The low-resolution MS bands are sharpened by injecting high-pass directional details extracted from the high-resolution panchromatic (Pan) image by means of the Wavelet and curvelet transform which is a non-separable MRA whose basis function are directional edges with progressively increasing resolution. The fusion of high-spectral but low spatial resolution multispectral and low-spectral but high spatial resolution panchromatic satellite images is a very useful technique in various applications of remote sensing. Some studies showed that waveletbased image fusion method provides high quality of the spectral content of the fused image. In this paper we introduce a new method based on the Wavelet and curvelet transform using Neuro-Fuzzy which represents edges better. Since edges play a fundamental role in image understanding one good way to enhance spatial resolution is to enhance the edges. Wavelet and curvelet-based image fusion method provides richer information in the spatial and spectral domains simultaneously. It will perform image fusion using Wavelet and curvelet Transform with Neuro-Fuzzy Algorithm. This new method has reached an optimum fusion result. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab Software. Keywords: Image Fusion, Curvelet Transform, Wavelet Transform, Neuro-Fuzzy, SVM, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE). INTRODUCTION The process of including complementary and redundant information from different images into one composite image which includes a better description of the underlying scene is known as image fusion and these results in a fused image more useful for human visual and machine processing. Image fusion strategies are basically classified into pixel level and region level approaches. Pixel level techniques: The set of pixels in the source image determine each pixel in the fused image. Basically pixel level techniques are classified into spatial domain and transform domain techniques. Region level techniques: In this technique the segmentation of the images into regions and then based upon the extracted region fusion is performed. The process of image fusion c o m b i n e s two or more images. Different images contain different information is the main idea behind image fusion. Wavelet transforms is that in which the transformation should allow only changes in time extension, but not shape. This is affected by choosing suitable basis functions that allow for these Changes in the time. Curvelet are an appropriate basis for representing images which is smooth apart from singularities along smooth curves where the curves have bounded curvature where objects have a minimum length scale in the image. This property holds for cartoons and geometrical diagrams and text. WAVELET TRANSFORM: Wavelet transforms is that in which the transformation should allow only changes in time extension, but not shape. This is affected by choosing suitable basis functions that allow for these Changes in the time. A signal analysis method similar to image pyramids is the discrete wavelet transform. The main difference is that while image pyramids lead to an over complete set of transform coefficients, the wavelet transform results in a no redundant image. The discrete 2-dim wavelet transform is computed by the recursive application of low pass and high pass filters in each direction of the input image followed by sub sampling. One major drawback of the wavelet transform when applied to image fusion is its well known shift dependency this results in inconsistent fused images when invoked in image sequence fusion. A wavelet transform array is synthesized for the product image and populated from the source images based on a set of predefined rules. After population, this synthetic array is inverse wavelet transformed to create the product image. Graham provides a more detailed discussion of the application of wavelet theory to image 31 | P age Australian Journal of Information Technology and Communication Volume II Issue I fusion. Our prototype system handles images which are co registered and the same size, with dimensions which are powers of two. A wavelet transforms using the Daubechies basis functions with filter lengths of 4, 12, or 20 are performed on all input images to be fused. A significant part of our work cantered on determining rules to use in combining wavelet transform array information. The system needed to allow operations to be performed on individual wavelet array blocks, so that low and high frequency components could be treated differently. The system needed to enable the use of many different combination rules. The approach taken to achieve this was to identify several primitive operations that would be required to implement a variety of combination rules. These primitive operations act upon individual frequency blocks in wavelet arrays or on whole wavelet arrays at once. The most useful primitive operation is to simply take the coefficient with the maximum amplitude from any input Image at each location in the wavelet transform array. Another operation is to average the values in all input wavelet transform arrays at each location. This operation, if performed on the entire wavelet array and inverse transformed, produces a result indistinguishable from the result of simply averaging the input images. We found this operator useful when performed on selected frequency blocks in combination with other operators. CURVELET TRANSFORM: The Curvelet transform like the wavelet transform is a Multiscale transform with frame elements indexed by scale and location parameters. Unlike the wavelet transform parameters and the Curvelet pyramid contains elements with a very high degree of directional specific city. In addition, the Curvelet transform is based on a certain anisotropic scaling principle which is quite deferent from the isotropic scaling of wavelets. The elements obey a special scaling law where the length of the support of a frame elements and the width of the support are linked by the relation width ¼ length2. All of these properties are very stimulating and have already leaded to a range of interesting idealized applications {for example in tomography and in scientific computation. An understanding of the Curvelet transform concept opens one's eyes to the fact that in two and higher dimensions new Multiscale representations are possible, having properties unavailable by wavelets and having stimulating structural features. While it is possible that this new idea will be quickly forgotten with the passage of time we feel that the very novel features of the transform anisotropy, anisotropy scaling - compel further investigation for the moment. The Curvelet transform (CVT) is a multi-scale transform proposed by Candes and Donoho and is derived from the Ridgelet transform The Curvelet transform is suited for objects which are smooth away from discontinuities across curves then the Fourier ISSN 2203-2843 Transform does not handle point's discontinuities well because a discontinuity point affects all the Fourier Coefficients in the domain. Moreover, Wavelet transform handles point discontinuities well and doesn't handle curve discontinuities well. Curvelet transform handles curve discontinuities well as they are designed to handle curves using only a small number of coefficients and Curvelet transform has several applications in various areas such as image denoising, image fusion, Seismic exploration, Turbulence analysis in fluid mechanics and so on. Curvelet Transformation is an enhancement technique to reduce image noise and to increase the contrast of structures of interest in image. Compared to other techniques, this method can manage the vagueness and ambiguity in many image reconstruction applications efficiently. NEURO-FUZZY (ANFIS): The Neuro-fuzzy term was born by the fusing of these two techniques Neuro and fuzzy. As each researcher combines these two tools in different way, then, some confusion was created on the exact meaning of this term. Still there is no absolute consensus but in general, the Neuro-fuzzy term means a type of system characterized for a similar structure of a fuzzy controller where the fuzzy sets and rules are adjusted using neural networks tuning techniques in an iterative way with data vectors (input and output system data). A Neuro-fuzzy technique called Adaptive network based fuzzy inference system (ANFIS) has been used as a prime tool. Adaptive network based fuzzy inference system (ANFIS) is a Neuro fuzzy technique where the fusion is made between the fuzzy inference system and the neural network. In ANFIS the parameters can be estimated in such a way that both the Sugeno and Tsukamoto fuzzy models are represented by the ANFIS architecture. Again with minor constraints the ANFIS model resembles the Radial basis function network (RBFN) functionally. This ANFIS methodology comprises of a hybrid system of fuzzy logic and neural network technique. The fuzzy logic takes into account the imprecision and uncertainty of the system that is being modelled [7]. Using this hybrid method, at first an initial fuzzy model along with its input variables are derived with the help of the rules extracted from the input output data of the system. Next the neural network is used to fine tune the rules of the initial fuzzy model to produce the final ANFIS model of the system. In this proposed work ANFIS is used as the backbone for the identification of real world systems. SUPPORT VECTOR MACHINE: It is primarily a classifier in which Width of the margin between the classes is the optimization criterion, i.e. empty area around the decision boundary defined by the distance to the nearest training patterns. These are called support vectors. The support vectors change the prototypes with the main difference between SVM and traditional template matching techniques is that they characterize the classes by a decision boundary which is 32 | P age Australian Journal of Information Technology and Communication Volume II Issue I not just defined by the minimum distance function. The concept of (SVM) Support Vector Machine was introduced by Vapnik. The objective of any machine that is capable of learning is to achieve good generalization performance, given a finite amount of training data. The support vector machines have proved to achieve good generalization performance with no prior knowledge of the data. The principle of an SVM is to map the input data onto a higher dimensional feature space nonlinearly related to the input space and determine a separating hyper plane with maximum margin between the two classes in the feature space. The SVM is a maximal margin hyper plane in feature space built by using a kernel function. These results in a nonlinear boundary in the input space and the optimal separating hyper plane can be determined without any computations in the higher dimensional feature space by using kernel functions in the input space. There are some commonly used kernels include:a) Linear Kernel ISSN 2203-2843 The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. PSNR represents a measure of the peak error. PSNR is the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation and the PSNR of the fusion result is defined as follows: PSNR = [ / ] (2) Where, R is the maximum fluctuation in the input image data type. Higher the value of the PSNR, better the performance of the fusion algorithm. PROPOSED WORK: Optimal Fusion Method The proposed work of image fusion is shown in the following figure:- K(x, y) = x, y b) Polynomial Kernel K(x, y) = (x. y+1) d SVM Algorithm i. ii. iii. Define an optimal hyper plane. Extend the above definition for non linear separable problems. Map data to high dimensional space where it is easier to classify with linear decision surfaces. PRINCIPAL COMPONENT ANALYSIS (PCA) PCA used for reducing the multidimensional data sets to lower dimensions PCA algorithm for the fusion of image is calculated the Eigen values and the Eigen vectors [17]. MEAN SQUARE ERROR (MSE) Mean Square Error (MSE) a commonly used reference based assessment metric is the Mean Square Error (MSE). The MSE represents the cumulative squared error between the reconstructed image and the original image is given by the following equation: MSE = ∑ [ ( , ) ( , )] ∗ (1) Where, M and N are the number of rows and columns in the input images, respectively. Lower the value of MSE the lower the error. PEAK SIGNAL TO NOISE RATIO (PSNR) Figure 1 Image Fusion using Neuro-Fuzzy Algorithm & SVM 33 | P age Australian Journal of Information Technology and Communication Volume II Issue I ISSN 2203-2843 RESULTS AND DISCUSSION In the following figures, result of proposed algorithm is highlighted. The comparison is made with the existing method. The existing method is based on Waveletcurvelet with PCA while in our methodology we have optimised the results by applying ANFIS and SVM. Figure 6 Graphical representation of RMSE Comparison Figure 2 CT Train Image Figure 7 Comparison of PSNR Figure 3 MR Train Image Figure 5 Comparison of RMSE Figure 8 Graphical representation of PSNR Comparison CONCLUSION The paper has presented a new trend in the fusion of digital image, MRI and CT images which are based on the Wavelet and Curvelet transform. A comparison study has been made between the traditional wavelet fusion algorithm and the proposed Curvelet fusion algorithm. The experimental study shows that the application of the Curvelet transform in the fusion of MR and CT images is superior to the application of the traditional wavelet transform. The obtained Curvelet fusion results have minimum MSE and PSNR than in wavelet fusion results. At last, these fusion methods are used to detect brain tumors. In vision, the Neuro-fuzzy algorithm is proposed for image fusion in this paper 34 | P age Australian Journal of Information Technology and Communication Volume II Issue I acquires better fusion result. In objective evaluation criteria, Curvelet fusion characteristic are superior to wavelet transform. REFERENCES [1] Vishal P.Tank, Divyang D. Shah,Tanmay V. Vyas, Sandip B. Chotaliya Manthan S. Manavadaria in 2013. They are purpose Image Fusion Based on Wavelet and Wavelet and curvelet Transform. [2] Jianwei Ma and Gerlind Plonka in 2012. He purpose A Review of Curvelets and Recent Applications. Multiresolution methods are deeply related to image processing, biological and computer vision, scientific computing, etc. ISSN 2203-2843 [9] J. L. Starck, E. J. Cand‘es and D. L. Donoho, “The curvelet transform for image denosing,” IEEE Trans. Image Processing, vol. 11, 2002, pp. 670-684. [10] J. L. Starck, E, J. Cand`es, and D. L. Donoho, “Gray and Color Image Contrast Enhancement by the Curvelet Transform,” IEEE Trans. Image Processing, vol. 12, no. 6, 2003, pp. 706-717. [11] E. J. Cand‘es, “Ridgelets: Theory and Applications,” Ph.D. Thesis, Department od Statistics, Stanford University, Standford, CA, 1998. [12] D. L. Donoho, “Digital ridgelet transform via rectopolar coordinate transform,” Stanford Univ., Stanford, CA, Tech. Rep, 1998. [3] Smt.G. Mamatha (Phd), L.Gayatri in 2012. They are purposed AN IMAGE FUSION USING WAVELET AND CURVELET TRANSFORMS. [13] D. L. Donoho, “Orthonormal ridgelets and linear singularities,” SIAM J. Math Anal., vol. 31, no. 5, 2003, pp. 1062-1099. [4] T. Ranchin and L. Wald, “Fusion of High Spatial and Spectral Resolution images: The ARSIS Concept and Its Implementation,” Photogrammetric Engineering and Remote Sensing, vol. 66, 2000, pp. 49-61. [14] M. I. Smith, J. P. Heather, "Fusion Technology Review of Image in 2005," Proceedings of the SPIE, Volume 5782, pp. 29-45, 2005. [5] L. Wald, T. Ranchin and M. Mangolini, “Fusion of Satellite images of different spatial resolution: Assessing the quality of resulting images,” Photogrammetric Engineering and Remote Sensing, vol. 63, no. 6, 1997, pp. 691-699. [6] J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala and R. Arbiol, “Multiresolution-based image fusion with addtive wavelet decomposion,” IEEE Transactions on Geoscience and Remote sensing, vol. 37, no. 3, 1999, pp. 1204-1211. [7] E. J. Cand‘es, “Harmonic analysis of neural networks,” A ppl. Comput.Harmon. Anal., vol. 6, 1999, pp. 197-218. [8] E. J. Cand`es and D. L. Donoho, “Curvelets- A surprisingly effective non adaptive representation for objects with edges,” in Curve and Surface Fitting: Saint-Malo, A. Cohen, C.Rabut, and L.L.Schumaker, Eds. Nashville, TN: Vanderbilt Univ. ersity Press, 1999. [15] Yong Yang, "Multi modal Medical Image Fusion through a New DWT Based Technique", 4th International Conference on Bioinformatics and Biomedical Engineering, pp 1-4, 2010. [16] Chandrakanth.R, SaibabaJ,Varadan.G, Raj.PA,"Fusion of High Resolution Satellite SAR and Optical Images "International Workshop on MultiPlatform/Multi-Sensor Remote Sensing and Mapping , pp 1-6, Jan 2011. [17] Anand, T. S., K. Narasimhan, and P. Saravanan. "Performance evaluation of image fusion using the Multi-Wavelet and Curvelet transforms." Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on. IEEE, 2012.