IMAGE FUSION TECHNIQUES

Transcription

1 Int. J. Chem. Sc.: 14(S3), 2016, ISSN X IMAGE FUSION TECHNIQUES A Short Note P. SUBRAMANIAN *, M. SOWNDARIYA, S. SWATHI and SAINTA MONICA ECE Department, Aarupada Veedu Insttute of Technology, CHENNAI (T.N.) INDIA ABSTRACT Image Fuson s the process of combnng nformaton from two or more mages of the same scene taken at the same nstant or at dfferent nstants to provde more detaled mages than the ndvdual mages separately. Image fuson has applcatons n dfferent areas lke commercal, mltary, medcal, remote sensng, urban development, agrcultural, etc. We present here the dfferent mage fuson technques and algorthms ncludng PCA, Wavelets, Shearlets and Noselets. Key words: Image fuson, PCA, IHS, DWT, Shearlets, Curvelets, Noselets. INTRODUCTION Image Fuson s the process of combnng nformaton from two or more mages of the same scene taken at the same nstant or at dfferent nstants to provde more detaled mages than the ndvdual mages separately. The mage fuson technques nvolve pxel based methods, decson based methods and feature based methods. The advantages and applcatons of mage fuson have been dscussed n lterature. In the followng sectons dfferent mage fuson technques are dscussed. The mage fuson started wth the IHS and PCA methods. Wth the advent of multresoluton analyss technques lke wavelets came nto force. Technques lke curvelets, shearlets were developed to overcome the dsadvantages faced by the wavelets. Technques ncludng Artfcal Neural networks, Fuzzy logc, soft computng were also ntroduced n the decson makng. Image fuson can be multmodal, multvew, multfocus or multtemporal. In multmodal fuson the nput mages are of dfferent sensors lke MRI and PET, MS and Panchromatc. Multvew fuson nvolves fuson of mages of the same modalty taken by the same sensor but at dfferent angles. Multtemporal fuson nvolves fuson of mages obtaned from same sensor obtaned at dfferent tmes. Image fuson technques can also be classfed as spatal doman * Author for correspondence; E-mal: subramanan@avt.ac.n

2 Int. J. Chem. Sc.: 14(S3), technques and frequency doman technques dependng on the doman n whch the fuson s carred out. Tradtonal methods The tradtonal methods lke averagng, maxmum or mnmum are smple to mplement and were the frst to be consdered. Here the pxel values of the ndvdual mages are consdered and the values of correspondng output pxels were taken as per the method. The averagng method nvolves takng the average of the correspondng pxels of the nput mages to obtan the value of the output pxels. Weghted superposton of pxel values has also been appled. The maxma method nvolves fndng the maxmum value of the correspondng nput pxels. The mnma method nvolves fndng the mnmum of the correspondng nput pxels. In Prncpal Component Analyss 1 the ntercorrelated data s converted nto a set of unrelated components called the prncpal components 2. Multresoluton analyss based methods The advent of multresoluton analyss led to the use of wavelets. The nput mages are transformed nto the frequency doman by wavelet transform. The frequency coeffcents are then merged based on dfferent rules lke mnmum, maxmum, average or soft computng technques lke ANN, Fuzzy etc.., Wavelets lke DWT, DTCWT have been dscussed n the lterature. Wavelet based fuson technques have been found to gve better results when compared wth the standard fuson technques consderng the spatal and spectral qualtes 3-5. The Fg. 1 1 shows an example of wavelet based fuson of MS and panchromatc mages. The wavelets can also be combned wth the tradtonal methods lke IHS, PCA to gve better results. PAN Image (H) MS Image (L) WT WT HA LA HL HL HH HH LA HL HH IWT FUSED IMAGE Fg. 1: Wavelet based fuson of multspectral and panchromatc mages

3 814 P. Subramanan et al.: Image Fuson. Curvelets are transforms that are hghly ansotropc. Hence the curvelets are better than wavelets n representng edges and are therefore well suted n the extracton of the detaled spatal nformaton from an mage 6. Wavelets lack drectonalty and are not good n capturng the geometrcal smoothness of the contours 7. Contourlet s an extenson of the wavelet and uses multscale and drectonal flter banks 7. The bass mages of the Contourlet transform are orented at varous drectons wth multple scales and also have flexble aspect ratos 7. Contourlets exhbt drectonalty and ansotropy. Contourlets need fewer coeffcents for representng a smooth contour n comparson wth wavelets. As explaned n by Mnh N Do et al. 8 the wavelets have square report only and requre more number whle the Contourlets can have elongated supports thereby requrng less number of coeffcents for effcent representaton of a smooth contour, as llustrated n Fg Contourlets are also computatonally effcent due to the terated flter banks. Contourlets have the ablty of capturng and lnkng the pont of dscontnutes n formng a lnear structure.e. contours. Fg. 2: Wavelets and contourlets n representng a smooth contour Noselet bass functons are constructed by twstng the translates and dlates of the mother functon 10. The sgnal s totally spread out n scale and n tme by the noselet transforms coeffcents. Hence nformaton pertanng to the orgnal sgnal s avalable n each subset of the noselet transformaton at all the scales and tmes 10. Shearlet transform has a sngle or fnte set of generatng functons and provdes almost optmal representatons for a large class of multdmensonal data 11. Shearlet also allows the contnuum and dgtal realms to be treated as unfed and has fast algorthmc mplementatons 11. Soft computng methods n mage fuson All fuson technques nvolve a step wheren the characterstcs are to be merged. In spatal doman pxel values are merged. In frequency doman the frequency coeffcents are merged. The mergng prncple s to be based on an algorthm or rule. Ths can be smple rules lke mnma, maxma, average, weghted average etc. or advanced algorthms lke fuzzy logc, artfcal neural networks, genetc algorthms, partcle swarm optmzaton etc.,

4 Int. J. Chem. Sc.: 14(S3), CONCLUSION Image fuson applcatons have ncreased manfold wth the advent of large number of mages beng captured by dfferent types of sensors. We have dscussed a few technques that are avalable n the lterature ncludng the tradtonal IHS, Brovey, PCA, Wavelets and advanced technques lke DTCWT, Curvelets, Noselets and Shearlets. REFERENCES 1. D. Jang, D. Zhuang, Y. Huang and J. Fu, Survey of Multspectral Image Fuson Technques n Remote Sensng Applcatons, Image Fuson and Its Applcatons, Dr. Yufeng Zheng (Ed.), ISBN: (2011). 2. M. Strat, S. Rahman and D. Merkurev, Evaluaton of Pan-Sharpenng Methods (2008). 3. M. Gonzáles Audícana and A. Seco, Fuson of Multspectral and Panchromatc Images Usng Wavelet Transform- Evaluaton of Crop Classfcaton Accuracy, n Proc. 22 nd EARSeL Annu. Symp. Geonformaton Eur.-Wde Integr., Prague, Czech Republc, T. Benes, Ed. (2003) pp K. Amolns, Y. Zhang and P. Dare, Wavelet Based Image Fuson Technques An Introducton, Revew and Comparson, ISPRS J. Photogrammetrc and Remote Sensng, 62, (2007). 5. J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala and R. Arbol, Multresoluton-Based Image Fuson wth Addtve Wavelet Decomposton, Geoscence and Remote Sensng, IEEE Transactons on, 37(3), (1999). 6. M. Cho et al., Fuson of Multspectral and Panchromatc Satellte Images Usng the Curvelet Transform, IEEE Geoscence and Remote Sensng Lett., 2(2) (2005). 7. D. D.-Y. Po and M. N. Do, Drectonal Multscale Modelng of Images Usng the Contourlet Transform, Image Processng, IEEE Transactons on, 15(6), (2006). 8. M. N.Do and M. Vetterl, The Contourlet Transform: An Effcent Drectonal Multresoluton Image Representaton, Image Processng, IEEE Transactons, 14(12), 2091, 2106 (2005). 9. M. N. Do and M. Vetterl, The Contourlet Transform: An Effcent Drectonal Multresoluton Image Representaton, Image Processng, IEEE Transactons, 14(12), (2005).

5 816 P. Subramanan et al.: Image Fuson. 10. K. Pawar et al., Multchannel Compressve Sensng MRI Usng Noselet Encodng, arxv: v2 [physcs.med-ph] (2014). 11. C. Duan, Q. Huang, X. Wang, S. Wang and H. Wang, Remote Sensng Image Fuson Based On IHS and Dual Tree Compactly Supported Shearlet Transform, Int. J. Sgnal Process., Image Processng and Pattern Recognton, 7(5) (2014). Accepted :