Image Fusion based on Wavelet and Curvelet Transform using ANFIS Algorithm

Transcription

1 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org Image Fuson based on Wavelet and Curvelet Transform usng ANFIS Algorthm Navneet Kaur 1, Madhu Bahl 2 1 M.Tech Student, Department of Computer Scence, CEC, Landran.Punjab(Inda) 2 Assstant Professor, Department of Computer Scence, CEC, Landran.Punjab(Inda) ABSTRACT Image Fuson s the process of combnng nformaton from two or more mages of a scene nto a sngle composte mage whch s more nformatve for vsual percepton. Image processng plays a vtal role n the medcal feld because most of the dseases are dagnosed by means of medcal mages. In ths paper the mage fuson based on wavelet and curvelet transform usng ANFIS algorthm s used to get more accurate results wth mproved mage qualty. MR (Magnetc Resonance) mages are used for the fuson purpose. The result generated by ANFIS algorthm s compared to PCA technque of mage fuson usng wavelet and curvelet transform. The parameters lke RMSE (Root Mean Square Error) and PSNR (Peak Sgnal to Nose Rato) are used to evaluate the performance measures. Keywords: Image Fuson, ANFIS, Wavelet and Curvelet transform, RMSE, PSNR. 1. INTRODUCTION The sensor technology s developng very fast, people obtan mages n more and more ways, and the types of mage fuson are also ncreasngly rch, such as the Image fuson of same sensor, the mult-spectral mage fuson of snglesensor, the mage fuson of the sensors wth dfferent types, and the fuson of mage and non-mage. The am of mage fuson n general s to use mages as redundant or complementary sources to extract nformaton from them wth hgher accuracy or relablty. Image fuson s becomng challengng feld and t s used n medcal magng, mltary and satellte magng. The three dfferent tradtonal data fuson are feature level, pxel level and decson level. It comes under the fuson of data categorzaton. 1.1 Image Fuson Image fuson s the process of combnng nformaton from two or more mages nto a sngle mage n computer vson. Mult-sensor [1] Image fuson s the process of combnng mages and to get more nformatve mages than any of the nput mages. In remote sensng applcatons, motvaton for dfferent mage fuson algorthms s gven by the ncreasng avalablty of space borne sensors. Hgh spatal and hgh spectral resoluton s requred n a sngle mage processng.. The fused mage can have complementary spatal and spectral resoluton characterstcs [2]. However, sometmes whle mergng the standard mage fuson technques can dstort the spectral nformaton of the Mult-spectral data. Two types of mages are avalable n satellte magng. The panchromatc mage acqured by satelltes s transmtted wth the maxmum resoluton avalable and the multspectral data are transmtted wth coarser resoluton, whch s two or four tmes lower [4]. 1.2 Image Fuson Technques In Image fuson technques the wavelet, curvelet and dscrete wavelet transform are dscussed here Wavelet Transform Wavelet transforms s that n whch the transformaton should allow only changes n tme extenson but not n shape or structure. The changes n tme are affected by choosng sutable bass functons [10]. Ths transform can handle pont dscontnue well than STFT, t s not optmal up to curve. Because the wavelet bass s sotropc and the curve have drecton so t take lot of coeffcents to account for edges Curvelet Transform Motvaton: The tme frequency analyss s decomposed a sgnal to several orthogonal bases. We can quantze the sgnal to the summaton of dfferent bass wth dfferent coeffcent. Wth the approach, t s easy to analyss the sgnal and there are some beneft [6] : Data compresson: Few coeffcent wth correspond bass domnant the sgnal. Quantze wth those domnant coeffcent can reach data compresson. Feature extracton : Bass wth large coeffcent s the feature of the sgnal. Checkng those bass s useful n pattern recognton. Volume 3, Issue 9, September 2014 Page 117

2 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org Image restoraton: For all bases are all orthogonal, t s easy to restoraton mage wthout effect of dependent Every transform has dfferent bass n tme frequency wth dffernet tlng. Edge Detecton An edge s a property attached to an ndvdual pxel and s calculated from the mage functon behavor n a neghborhood of the pxel. Edge Detecton s also regarded as a vector varable (magntude of the gradent, drecton of an edge). The purpose of edge detecton n general s to sgnfcantly reduce the amount of data n an mage [7] Dscrete wavelet transform The transform of a sgnal s just another form of representng the sgnal. The nformaton content present n the sgnal does not effected n t. The Wavelet Transform provdes a tme frequency for representng the sgnal [8]. The multresoluton technque used n t whch shows that dfferent frequences are analyzed wth dfferent resolutons. The Dscrete Wavelet Transform (DWT), whch s based on sub band codng, and also found as fast computaton of Wavelet Transform. It s easy to mplement and reduces the computaton tme and requrement of resources. Wavelet transform fragmented a sgnal nto a set of basc functons. The wavelets are bass functons. Wavelet approach (More coeffcents are needed) Curvelet approach (Fewer coeffcents are needed) Fgure 1: Wavelet and Curvelet approach 1.3 Applcatons of Image Fuson Remote sensng technques are proved to be powerful tools for the montorng of the Earth s surface and atmosphere on a global, regonal, and even local scale, by provdng mportant coverage, mappng and classfcaton of land cover features lke vegetaton, sol, water and forests. It has been used n many felds of remote sensng, such as object dentfcaton, classfcaton, and change detecton. The followng paragraphs descrbe the recent achevements of mage fuson n more detal Object dentfcaton The feature enhancement capablty of mage fuson s vsually apparent n VIR/VIR combnatons that results n mages that are superor to the orgnal data. To maxmze the amount of nformaton extracted from satellte mage data, useful products can be found n fused mages [9]. A Dampster-Shafer fuson method for urban buldng detecton was presented n 2004; mult-spectral aeral magery had been used. Apart from buldngs the classes, tree, grass land, and bare sol are also dstngushed by a classfcaton method of data fuson. Identfcaton of lnear objects such as roads also benefted from mage fuson technques. Image fuson s playng fundamental role n these applcatons specally Classfcaton Classfcaton s one of the key tasks of remote sensng applcatons. The classfcaton accuracy of remote sensng mages s mproved when multple source mage data are ntroduced to the processng [9]. Image fuson methods wll lead to strong advances n land use/land cover classfcatons by use of the complementary of the data presentng ether hgh spatal resoluton or hgh tme repettveness Change detecton Change detecton s the process of dentfyng dfferences n the state of an object or phenomenon by observng t at dfferent tmes. Change detecton s an mportant process n managng and montorng natural resources and urban development because t provdes quanttatve analyss of the spatal dstrbuton of the populaton of nterest. Image fuson for change detecton takes advantage of the dfferent confguratons of the platforms carryng the sensors. The mergng of these temporal mages enhances nformaton on changes that mght have occurred n the observed area. Volume 3, Issue 9, September 2014 Page 118

3 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org 1.4 Artfcal Neural Networks The ANFIS s the combnaton of ANN (Artfcal Neural Network) and Fuzzy logc. These are composed of nterconnectng artfcal neurons (programmng constructs that mmc the propertes of bologcal neurons). They may ether be used to gan an understandng of bologcal neural networks and for resolvng artfcal ntellgence queres wthout necessarly creatng a model of a real bologcal system [10]. The real bologcal nervous system s hghly complex: artfcal neural network algorthms attempt to abstract ths complexty and focus on what may hypothetcally matter most from an nformaton processng pont of vew. The basc archtecture conssts of nput, output and hdden types of neuron. In these networks, the sgnal flows from nput to output unts n a feed-forward drecton. The processng of data can extend over the multple layers, but there s no feedback connecton. Feedback connectons are present n recurrent networks. Contrary to feed-forward networks, the dynamcal propertes of the network are to be needed. In some cases, the actvaton values of the unts undergo a relaxaton process such that the network wll evolve to a stable state n whch these actvatons do not change anymore. X1 Teach/Use X2 Neuron Output Y Xn Fgure 2: Archtecture of Neural Network Feed Forward Neural Networks Feed-forward ANNs allow sgnals to travel n one way only; from nput to output. There s no feedback (loops).e. the output of any layer does not affect that same layer. Feed-forward ANN assocate nput wth output [12]. It s used n pattern recognton. Ths type of organsaton s also Known as bottom-up or top-down. Sngle-layer perceptron, multlayer perceptron and radal bass Functons are types of feed forward neural networks. Sngle Layer Perceptron It s the smplest knd of neural network, whch conssts of a sngle layer of output nodes; the nputs are fed drectly to the outputs va a seres of weghts. In ths way t s consdered the smplest knd of feed-forward network [12]. Then, the product of the weghts s ntegrated by addng and the nput s calculated at each and every node. If the value s above some threshold (typcally 0) the neuron fres and takes the actvated value (typcally 1), otherwse t takes the deactvated value (typcally -1). Neurons wth ths knd of actvaton functon are also called artfcal neurons or lnear threshold unts. Back Propagaton It s a supervsed learnng method, and s a generalzaton of the delta rule [12]. It requres a dataset of the desred output for many nputs, makng up the tranng set. It s most useful for feed-forward networks (networks that have no feedback, or smply, that have no connectons that loop). The term s an abbrevaton for "backward propagaton of errors". Back propagaton requres that the actvaton functon used by the artfcal neurons (or "nodes") be dfferentable. 1.5 Fuzzy Logc Fuzzy logc s an approach based on "degrees of truth" to compute rather than the usual "true or false" (1 or 0) Boolean logc on whch the modern computer s completely based. The concept of Fuzzy Logc (FL) was conceved by Lotf Zadeh, a professor at the Unversty of Calforna at Berkley, and presented not as a control methodology, but as a way of processng data by allowng partal set membershp rather than crsp set membershp or non-membershp. FL s a problem-solvng control system methodology that lends tself to mplementaton n systems rangng from smple, small, embedded mcro-controllers to large, networked, mult-channel PC or workstaton-based data acquston and control systems. It can be mplemented n hardware, software, or a combnaton of both. It provdes a smple way to arrve at a defnte concluson based upon vague, ambguous, mprecse, nosy, or mssng nput nformaton. Its approach to control Volume 3, Issue 9, September 2014 Page 119

4 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org problems mmcs how a person would make decsons, much faster. Ths knd of process s used n artfcal computer neural network and expert systems. 2. RELATED WORK In ths paper the exstng work usng PCA technque s ntroduced wth wavelet and curvelet transform. The lterature survey related to ths work s also dscussed here. 2.1 PCA (Prncpal Component Analyss) algorthm Prncpal component analyss (PCA) s a novel scheme for dmenson reducton and s used for mage fuson [11]. It s also a vector space transform used for reducng the Multdmensonal data sets to lower dmensons. The PCA algorthm for the fuson of mages s dscussed as follows. Generate the column vectors from the nput mage matrces. Calculate the covarance matrx of the two column vectors produce. The dagonal elements of the 2x2 covarance matrx contan the varance of each column vector wth tself, respectvely. Calculate the Egen values and the Egen vectors of the covarance matrx. Normalze the column vector correspondng to the larger Egen value by dvdng each element wth mean of Egen vector. The output values of the normalzed Egen vector act as the weght values whch are respectvely multpled wth each pxel of the nput mages. Sum of the two scaled matrces calculated n the Prevous step wll be the fused mage matrx [12]. Ths work has been showed n curvelet transform for the mage fuson and after studyng ths technque, the comparson of ANFIS s done wth PCA algorthm. PCA technque shows low mage qualty and to enhance the mage qualty ANFIS algorthm s used. 2.2 Lterature Candes et al. (2000) [9] proposed the medcal mages wth Wavelet and Curvelet transform based approach for the fuson of dgtal mage. Here ts looked at the selecton prncples about low and hgh frequency coeffcents accordng to dfferent frequency doman after Wavelet and Curvelet Transform. Medcal mages have several objects and curved shapes, t s expected that the curvelet transform would be better n ther fuson. The smulaton results show the superorty of the curvelet transform to the wavelet transform n the fuson of dgtal mage and MR and CT mages from entropy, correlaton coeffcents and the RMSE pont of vew. In vson, the fuson algorthm proposed n ths paper acqures better fuson result. Narasmhan et al. (2012) [13] proposed performance evaluaton of mage fuson usng the mult-wavelet and curvelet Transform. In fuson algorthm, the nput mages from two dfferent modaltes such as CT and MR are ntally regstered and then transform nto Mult- Wavelet transform and Curvelet transform are appled on the nput mages. The mages produced from dfferent medcal magng methods such as Computer Tomography (CT) and Magnetc Resonance (MR) mages are merged to get a new fused mage to mprove the qualty of mage for dagnoss. Fnally the resultant mages are fused usng varous fuson technques Prncpal Component Analyss, Laplacan Pyramd etc. Results are evaluated and compared accordng to four measures of performance the Entropy (H), Root Mean Square Error (RMSE) and Peak Sgnal to Nose Rato (PSNR). Loganathan et al. (2013) [10] proposed a hybrd learnng mechansm that utlzes the tranng and learnng neural networks to fnd parameters of a fuzzy system based on the symptoms created by the mathematcal model. The man am of ths work s to determne approprate neural network archtecture for tranng the ANFIS structure n order to adjust the parameters of learnng method from a gven set of nput and output data. The tranng algorthms used n the work are ANFIS and Runge-Kutta Learnng Algorthm (RKLM). The ANFIS shows the better results than the other algorthm. 3. METHODOLOGY The methodology of work starts wth the overvew of ANFIS algorthm and mplements the wavelet and curvelet algorthm on nput MR medcal mages. The result of usng an enhanced ANFIS algorthm nterpreted on the bass of dfferent qualty parameters. The results wth proposed algorthm are compared wth the PCA technque. The result acqured by the proposed algorthm proved better qualty of the fused mage. Algorthms used for the proposed work are as gven below: A. Wavelet transform algorthm B. Curvelet transform algorthm Volume 3, Issue 9, September 2014 Page 120

5 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org C. ANFIS (hybrd learnng algorthm) A. Wavelet Transform Algorthm The specfc operatonal procedure for the wavelet based mage fuson approach s now summarzed as follows: 1. The two nput mages are regstered ntally. 2. Each nput mage s analyzed and a set of wavelet Coeffcents are generated. 3. The maxmum frequency rule s used for the fuson of wavelet coeffcents. 4. The nverse wavelet transform step s performed (The fused coeffcents are subjected to the nverse wavelet transform) to obtan the fused mage. The graphcally representaton of the algorthm s shown n fg.3. Fgure 3: Wavelet Transform based Image fuson B. Curvelet Transform Algorthm The algorthm for fusng mages usng the Curvelet transform s explaned as follows: 1. The two nput mages are ntally regstered ntally. 2. Each nput mage s then analyzed and a set of Curvelet coeffcents are generated. 3. The maxmum frequency rule s used for the fuson of Curvelet coeffcents. 4. Fnally the Inverse Curvelet transform (ICVT) step s performed (The fused coeffcents are subjected to the nverse Curvelet Transform) to obtan the fused mage. The graphcal representaton s shown n fg 4. Fgure 4: Curvelet transform based mage fuson C. ANFIS algorthm ANFIS s known as Adaptve Network Based Fuzzy Inference System. It has rules and membershp functons. The ANFIS approach learns the rules and membershp functons from data. ANFIS s an adaptve network. An adaptve network s network of nodes and drectonal lnks assocated wth the network s a learnng rule - for example back propagaton. It s called adaptve because some, or all, of the nodes have parameters whch affect the output of the node. These networks are learnng a relatonshp between nputs and outputs. Fgure 5: An ANFIS archtecture Volume 3, Issue 9, September 2014 Page 121

6 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org A Two Rule Sugeno ANFIS has rules of the form: If x s A and If x s A 1 y s B1 THEN f1 p1 x q1 y r1 2 and y s B2 THEN f 2 p2 x q2 y r2 For the tranng of the network, there s a forward pass and a backward pass. We now look at each layer n turn for the forward pass. In forward pass the nput vector propagates through the network layer by layer. In the backward pass, the error or bug s sent back through the network n smlar manner to back propagaton. The functons of all layers are descrbed as follow: Layer 1 The output of each node s: O1, A ( x) for 1,2 O ( ) Essentally the membershp grade for x and y. 1, x The membershp functons could be anythng but for llustraton purposes we wll use the bell shaped functon gven by: A ( x ) 1 1 x c Where a, b, c are parameters. These are called the premse parameters. Layer 2 Every node n ths layer s fxed. Ths s where the t-norm s used to AND the membershp grades - for example the product: O2, w ( x) ( y), 1,2 A B Layer 3 Ths layer contans fxed nodes whch calculate the rato of the frng strengths of the rules. O3, w w1 w2 Layer 4 In ths layer the nodes are adaptve and perform the consequent of the rules. O a w 4, 2 b wf w( pxqy r ) The parameters n ths layer ( p, q, r ) are to be determned and are referred to as the consequent parameters. Layer 5 There s a sngle node that computes the complete and overall output. O 5, O1, B ( y) for 3,4 2 w f Typcally, the nput vector s fed through the network layer by layer. In ths way the whole output s aggregated at the end and helps n mprovng the mage qualty. 3.1 Algorthm Level Desgn In the desgn phase the flow chart of the methodology s dscussed. ANFIS s the enhanced algorthm than the exstng algorthms. It starts by loaded frst mage then preprocessng s done. Now, the second mage s loaded.e. MR mage. After that ANFIS algorthm s appled. Then, wavelet and curvelet algorthms are appled. Now, the results are compared by usng two parameters (RMSE & PSNR) wth the exstng results. The flowchart to graphcally represent the steps of proposed algorthm s shown n Fgure 6. w w f Volume 3, Issue 9, September 2014 Page 122

7 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org Fgure 6: Flow Dagram of methodology 3.2Parameters Used PSNR PSNR s the rato between the maxmum possble power of a sgnal and the power of corruptng nose that affects the fdelty of ts representaton. The PSNR of the fuson result defned as follows: ` Where s the maxmum gray scale value of the pxels n the fused mage. Hgher the value of the PSNR, better the performance of the fuson algorthm RMSE A commonly used reference based assessment metrc s the Root Mean Square Error (RMSE). The RMSE between a reference mage R and a fused mage F, s gven by the followng: Were R (m, n) and F (m, n) are the reference (MR) and fused mages, respectvely and M and N are mage dmensons. Smaller the value of the RMSE, better the performance of the fuson algorthm. 4.EXPERIMENTAL SET UP AND RESULTS To see the qualtatvely as well as quanttatvely performance of the proposed algorthm mages of jpg format has been used. The mage fuson of the medcal mages s done wth the exstng technque.e. Wavelet and Curvelet technque and the enhanced ANFIS technque. The mages shown n the fg.7 (a) and 7(b) are nput mages and fg 7(c) shows resultant mage usng ANFIS algorthm. In ths paper ANFIS algorthm s used to acqure the better mage qualty than the exstng results. Ths technque has sgnfcantly mproved the results. Fgure 7: (a) I1 Input mage Fgure 7: (b) I1 Input mage Volume 3, Issue 9, September 2014 Page 123

8 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org Fgure 7(c): Result mage usng mage fuson The Table 1.1 shows the results of the Image fuson based on curvelet and wavelet based usng the ANFIS algorthm. The mproved results usng the proposed technque are hghlghted to show the comparson. As shown n Table 1.2 the Image fuson usng the technques (wavelet and curvelet transform and ANFIS proposed method) s appled on the mages. The PSNR and RMSE usng proposed method s a very good mprovement as compared to the exstng technque. Table 1.1: Comparson table of the mage fuson technques usng parameters wth the exstng values IMAGE NAME PREVIOUS VALUE OF RMSE PROPOSED VALUE OF RMSE I1.JPG IMAGE NAME PREVIOUS VALUE OF PSNR PROPOSED VALUE OF PSNR I1.JPG The table result proved that the proposed technque enhance the result of the fused mage. 4.1Graphcal Representaton The fgure 8 shows the comparson of the proposed algorthm to the exstng algorthm. Here, the RMSE havng low value and PSNR shows hgher value by applyng proposed algorthm. Fgure 8: Column-chart of I1 mage Volume 3, Issue 9, September 2014 Page 124

9 Internatonal Journal of Applcaton or Innovaton n Engneerng & Management (IJAIEM) Web Ste: Emal: edtor@jaem.org 5. CONCLUSION Image fuson process s greatly enhanced by usng ANFIS algorthm. Ths research has presented a new trend n the fuson of MR mages whch s based on wavelet and curvelet transform wth ANFIS algorthm. The expermental study shows that the applcaton of wavelet and curvelet transform wth ANFIS algorthm s superor to the exstng technque. A comparatve study has been made between the exstng work and the proposed work. From the results t s concluded that the proposed algorthm enhances the performance wth hgher PSNR and lower RMSE value. The study on ths research can be extendng to work on more than two mages smultaneously. Also n future more parameters lke by enhancng the number of pxels qualty can be consdered. Ths study can further apply new formulas or algorthm for the enhancement of parameters n fuson of mages land reducng tme for executon. REFERENCES [1] Chandrakanth, R. and Sababa, J. "Fuson of Hgh Resoluton Satellte SAR and Optcal Images", Internatonal Workshop on Mult-Platform/Mult-Sensor Remote Sensng and Mappng, pp. 1-6, [2] Mamatha, G. and Gayatr, L. "An mage fuson usng wavelet and curvelet transform", Global Journal of Advanced Engneerng Technologes, vol.1, Issue-2, ISSN: , [3] Ranchn, T. Fuson of Satellte mages of dfferent spatal resoluton: Assessng the qualty of resultng mages, Photogrammetrc Engneerng and Remote Sensng, Vol. 63, pp , [4] Jnzhu, Y. and Fangfang, H. "A Block Advanced PCA Fuson Algorthm Based on PET/CT", Internatonal Conference on Intellgent Computaton Technology and Automaton, Vol. 2, pp , [5] Kaur, D. and Maan, P. Medcal mage fuson usng Gaussan flters wavelet transform and curvelet transform, Internatonal Journal of Engneerng Scence & Advanced Technology, ISSN: Vol. 4, Issue-3, pp , [6] Janwe, M. and Gerlnd, P. "A Revew of Curvelet and Recent Applcatons, Multresoluton methods are deeply related to mage processng, bologcal and computer vson, scentfc computng, etc, [7] Starck, J. and Donoho Gray and Color Image Contrast Enhancement by the Curvelet Transform, IEEE Trans. Image Processng, vol. 12, no. 6, pp , [8] Yuhu, L. and Jnzhu, Y. "PET/CET medcal mage fuson based on multwavelet transform", 2nd Internatonal Conference and Advanced Computer Control, vol.2, pp , [9] Candes and Donoho The Curvelet transform for mage denosng, IEEE Trans. Image Processng, vol. 11, pp , [10] Loganathan, C. and Grja, K. Hybrd Learnng for Adaptve Neuro Fuzzy Inference System, Internatonal Journal of Engneerng and Scence vol.2, Issue 11, pp 06-13, [11] Patl, U. Image Fuson Usng Herarchcal PCA, Internatonal conference on Image Informaton Processng, pp. 1-6, [12] Zhang, Q., Chen, Y. and Katayama, M. Addtve and Multplcatve Nose reducton by back propagaton Neural Network, Conference of IEEE, pp , [13] Narasmhan, K. and Saravanan P. Performance Evaluaton of Image Fuson Usng the Mult-Wavelet and Curvelet Transforms, Internatonal Conference on Advances n Engneerng, Scence and Management, pp , 2012 Volume 3, Issue 9, September 2014 Page 125