Developing an Efficient Algorithm for Image Fusion Using Dual Tree- Complex Wavelet Transform

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1 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 Developing an Efficient Algoritm for Image Fusion Using Dual Tree- Complex Wavelet Transform Roit Raj Sing, Ravi Misra IV Sem M.E Student, Dept. of ECE, FET-SSGI, Bilai (C.G), India Sr. Asst. Prof, Dept. of EEE, FET-SSGI, Bilai (C.G), India ABSTRACT:Image Fusion is an image processing tecnique for combining data from two or more images of te same frame into a single image wic will retain all te important features of te input images. Te objective of image fusion includes set of images taken from different domain of te same scene into a single output image wic is more informative, valid, reliable and accurate as compared to eac source images. Fusion tecniques are important in detection and treatment of cancer in medical field, remote sensing and robotics. Tis paper focuses on developing an image fusion tecnique using Dual Tree Complex Wavelet Transform. Te results sowcase tat te suggested algoritm as a more desirable visual quality tan te base tecnique. Also te standard of te fused image as been estimated using a set of quality metrics. KEYWORDS: Image fusion; DT-CWT; WT; Quantitative metrics; fusion rules I. INTRODUCTION Image fusion is a process wic collects data from two or more images of te same frame into a singleimage wic provides more detailed information compared to eac single input image and can provide more valid and efficient result for te observers. Today, image fusion tecniques are used in various fields suc as remote sensing, optical microscopy, robotics and medical imaging []. Tere are many tecniques for image fusion. Tese tecniques can be categorized into two fundamental groups namely spatial domain metods [] and transform domain metods [, 3]. For spatial domain tecniques, we can refer to simple averaging metods, PCA [4], linear fusion. Tese tecniques are easy but suffer from spatial deformation and do not present any spectral information. Te stated drawbacks motivated us to use te transform domain metods. Discrete wavelet transform (DWT) as been widely used for image fusion recently, but it as some problems as it doesn't provide sufficient directional information and resulted in an image wit sift variance and additive noise [5, 6]. As an alternative in te present work we propose a new sceme based on DT- CWT wic provides approximate sift invariance and more directional information wic first is introduced by Kingsbury. Tis transform overcomes te DWT limitations [7, 8]. After image decomposition using DT-CWT, we apply maximum selection and local energy to mix low and ig frequency coefficients respectively [9, 0]. Ten inverse DTCWT was applied on te fused coefficients to obtain te fused image. Finally te proposed metod is compared wit DWT and some spatial domain tecniques suc as simple averaging and PCA using various quantitative metrics suc as SSIM, MI, entropy, STD and average gradient (AVG) were bot qualitative and quantitative comparisons demonstrated tat te proposed sceme as a superior performance. In tis paper section deals wit te principle of te Dual Tree Complex Wavelet Transform. Section 3 involves different image quality metrics. II. RELATED WORK Uses Wavelet Transform based on maximum selection rule, good in area of Medical field for MRI []. Teeffectiveness of te fusion rule is determined by te objective evaluation of te fusion performance using parameters suc as te entropy, mutual information and te SSIM based index. From te experiments conducted and uman observation of te fused images we conclude tat te proposed PCA - Max fusion algoritm is an efficient tecnique for fusing IR and visible spectrum images [4].Te SIDWT sceme resulted in te most stable sequence, wile DWT and Laplacian image pyramid fusion sequences exibited flickering distortions, due to te sift variance of Copyrigt to IJIRCCE /ijircce

2 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 te decomposition process [6]. Image fusion as been attracted more attention in recentdecades especially in multimodal domain. As eac source image provides a limited aspect of information it's necessaryto combine tem in suc a way tat carry complementaryinformation of eac source images into te output fused image []. III. PRINCIPLE OF THE DUAL TREE COMPLEX WAVELET TRANSFORM DT-CWT replaces single tree DWT structure wit a dual tree real valued filters. Te two parallel trees filters and down-sample te incoming input signal in te same way as DWT, but since tere are two rater tan one filtering tree, te aliasing effect tat causes sift dependency in DWT get eliminated in DT-CWT. Discrete wavelet transform as been widely used in multi sensor image fusion, but it suffers from few major drawbacks suc as poor directional selectivity and sift variance as discussed earlier in tis paper. Tese mentioned problems were rectified by DT-CWT. At eac level DT-CWT one of te trees produces te real part wile te oter tree produces te imaginary part of te complex wavelet coefficients. Filters in eac tree are real-valued and te concept of complex coefficients only appears wen outputs from te two trees are merged []. Te inclusion of te second filter bank increases te redundancy of te transform to : for a D signal. A D dual-tree complex wavelet can be defined as (x, y) = (x) (y), were (x) and (y) are two complex wavelets, (x) = ( x) j ( x) and (y) = ( y) j ( y), ( x ) and ( y) are real wavelet transforms of upper filter bank and lower filter bank, g respectively. Ten we get te following equations: ( x, y) [ ( x) j ( x)][ ( y) j ( y)] ( x) ( y) ( x) ( y) j[ ( x) ( y) ( x) ( y)] () g g g g g g Te real parts of six oriented complex wavelets of DT-CWT are as follows: i( x, y) (, i( x, y), i( x, y)) () i 3( x, y) (, i( x, y), i( x, y)) (3) For i=, and 3 we ave:,( x, y) ( x) ( y),,( x, y) ( x) ( y) (4) g g,( x, y) ( x) ( y),, ( x, y) ( x) ( y) (5) g g,3( x, y) ( x) ( y),,3( x, y) ( x) ( y) (6) g g were Te imaginary parts of six oriented complex wavelets of DT-CWT are as follows: i( x, y) ( 3, i( x, y) 4, i( x, y)) (7) i 3( x, y) ( 3, i( x, y) 4, i( x, y)) (8) For i=, and 3 we ave: 3,( x, y) ( x) ( y),,( x, y) ( x) ( y) (9) g g,( x, y) ( x) ( y),, ( x, y) ( x) ( y) (0) g g,3( x, y) ( x) ( y),,3( x, y) ( x) ( y) () g g g Copyrigt to IJIRCCE /ijircce

3 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 Fig.- DT-CWT Structure In te above equations ( x ) and ( x) denote te low pass filters of te upper filter bank and lower filter bank wile, g ( y ) and ( y ) represents te ig pass filter of te upper filter bank and lower filter bank respectively. Eac of te g sub-band images contains te wavelet coefficients for bot imaginary and real parts in te ±5degree, ±45degree and ±75degree directional edges in te original image [7]. It can be seen tat te DT-CWT structure, comprise bot real and complex coefficients. It is well known tat DT-CWT tecnique is more suitable to visual sensitivity. Fusion policy involves te formation of a fused pyramid making use of DT-CWT coefficients wic are taken from te decomposed pyramids of te input images. Fusion Algoritm: a) Decomposition: Wit te elp of DT-CWT we start decomposing one of te input image and find approximated (LL, LH, HL) and detail [5 0,45 0,75 0, -5 0,-45 0 and ] bands. Repeat te same process for all te given input images. b) Pyramid formation: On te above obtained output decomposition is again done after te approximation tus it generates a series of different resolution pyramids. c) Baseband Fusion: Low frequency, ig low frequency information is present in te base band. Do masking on k k corresponding bands ten denote tese filtered bands as A ( i, j) and B ( i, j) for p t direction band and k t level. Take coefficient suc tat absolute value of filtered image at spatial location is ig enoug. p p Copyrigt to IJIRCCE /ijircce

4 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 k k km km F ( i, j) { A ( i, j), if A B ( i, j) p p p p k { B ( i, j), oterwise p () Fig.- DT-CWT based fusion IV. QUANTITATIVE IMAGE QUALITY METRICS Quality is a feature tat measures recognized image deterioration i.e., in comparison wit ideal image. Evaluation forms act as a crucial part in te evolution of image fusion tecniques. It consist full Reference metods, were efficiency is measured in contrast wit ideal image and No Reference Metods, i.e. no reference image. Tis paper deals wit te full reference Metods. Te metrics used are sown below in Table. Assumptions used in te given equations are as, A is te image wic is perfect, B is te resultant image, (i, j) is te pixel row and column index. ) Mean Square Error (MSE) MSE ( A B ) mn i 0 j 0 ) Peak Signal to Noise Ratio (PSNR) peak PSNR 0x log 0( ) MSE Copyrigt to IJIRCCE /ijircce

5 ISSN(Online): ISSN (Print): ) Average Difference (AD) AD ( A B ) mn i 0 j 0 4) Structural Content (SC) SC m n ( A ) ij ( B ) ij (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 5) Normalized Cross Correlation (NCC) NCC m n ( A B ) ( A ) ij 6) Maximum Difference (MD) MD max( A B ), i 0,,,... m; j 0,,,... n 7) Normalized Absolute Error (NAE) NAE m n ( A B ) ( A ) ij 8) Structural Similarity Index Metric (SSIM) It compares local patterns of pixel intensities wic ave been normalized for luminance and contrast and it provides a quality value in te range [0, ]. It consist parameters like K vector aving constant value in SSIM index and L indicate te dynamic range of values. K= [ ], L=55 by default. Tus te C and C will be C K L C K L G is te Gaussian Filter Window in default and te input images are filtered using G generating μ and μ. AG BG.. Tus,, are calculated and are A. G ij AB ij B. G From tese values te SSIM index is computed and SSIM mean( C / ( C C )) Copyrigt to IJIRCCE /ijircce

6 MSE PSNR AD SC MD NAE NCC SSIM ISSN(Online): ISSN (Print): ) Mutual Information (MI): Comparison Sown in table base on formula PBA( i, j) MI ( B, A) P (, )log[ ] i, j BA P ( i) P ( j) Metods (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 B A V. EXPERIMENTAL RESULTS Image Quality Metrics MSE PSNR AD SC MD NAE NCC SSIM Minimum SVD Average PCA Maximum Proposed Metod(DT- CWT) Table [] Minimum SVD Average Image Quality Metrics Fig.-3 Grap sowing Comparison between Different Image Fusion Tecnique Image Metrics Values () () (3) (4) (5) (6) Fig.-4Fused images using different algoritms (()-(6) are fused images, te metods used from () to (6) are: Average, Maximum, Minimum, PCA, SVD and Propose Metod (DT-CWT) Copyrigt to IJIRCCE /ijircce

7 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April Minimum SVD Average PCA Maximum Proposed Metod PSNR MSE Fig.-5 Grap Sowing relation between MSE and PSNR of different Image Fusion Tecnique Metods Image Quality Metrics Mutual Information (MI) Average PCA DWT Proposed Metod (DT-CWT) 6.5 Table [3] Image Quality Metrics MI Average PCA DWT Proposed Metod (DT-CWT) Image Quality Metrics MI Fig.-5 Grap sowing Comparison between Different Image Fusion Tecnique Image Mutual Information Value VI. CONCLUSION& FUTURE WORK Image fusion as drawn more attention in recent years. As eac source image gives a limited amount of information it's necessary to mix tem in suc a way tat tey carry complementary information of eac source images into te resultant fused image. From te above table of Quality metrics for different images it is clear tat te DT-CWT based fusion is aving a better result of PSNR wen compared to oter fusion tecnique, wic sows tat it is a better fusion tecnique as compared to oter general tecniques. Similarly if we ceck te MSE value, it is te least in DT-CWT. Tus it proves proposed metod performs te better in comparison to oter image fusion tecnique, since it reduces te drawback of sift invariance and it provides multiscale edge information and pase information. Visual analysis as also proved tat te proposed metod as a better performance compared to te oter metods. Copyrigt to IJIRCCE /ijircce

8 ISSN(Online): ISSN (Print): (An ISO 397: 007 Certified Organization) Vol. 3, Issue 4, April 05 Te proposed gives best result among previous tecniques for image fusion, owever it can also be extended for colour image fusion. Tere is a scope to develop robust joint segmentation algoritm and ence better feature level image fusion. Tis metod can be furter used to detect te accuracy of te any existing metodology. REFERENCES. A. Gostasby, -D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, Wiley Press, H. Li, S. Manjunat, S. Mitra, Multisensor image fusion using te wavelet transform, Grapical Models and Image Processing 57 (3) (995) S.G. Nikolov, P. Hill, D.R. Bull, C.N. Canagaraja, Wavelets for image fusion, in: A. Petrosian, F. Meyer (Eds.), Wavelets in Signal and Image Analysis, Kluwer Academic Publisers, Te Neterlands, 00, pp S. Sentil Kumar and S. Muttan, PCA Based Image Fusion, Proceedings of SPIE, vol. 633, pp.633t- to 633T Amolins, Krista, Yun Zang, and Peter Dare. "Wavelet based image fusion tecniques An introduction, review and comparison." ISPRS Journal of Potogrammetry and Remote Sensing 6, no. 4 (007): Oliver Rockinger, "Image sequence fusion using a sift invariant wavelet transform", International conference on image processing, vol.3, pp.8, October N. Kingsbury. Te dual-tree complex wavelet transform: a new tecnique for sift invariance and directional filters. In IEEE Digital Signal Processing Worksop, volume 86, Kingsbury, N.G.: Sift Invariant Properties of te Dual-Tree Complex Wavelet Transform. Proceedings of International Conference, ICASSP 99, Poenix, Arizona, USA, Vol., Mar. 999, pp Hill, Paul R., Cedric NisanCanagaraja, and David R. Bull. "Image fusion using complex wavelets." In BMVC, pp Lewis, Jon J., Robert J. O Callagan, Stavri G. Nikolov, David R. Bull, and NisanCanagaraja. "Pixel-and region-based image fusion wit complex wavelets." Information fusion 8, no. (007): Sruty S, Dr. LataParameswaran, Dept. of Computer Science and Engineering AMRITA ViswaVidyapeetam, Coimbatore, India Ajees P Sasi Dept. of Electronics Caarmel Engineering College Patanamtitta, Image Fusion Tecnique using DT-CWT, India /3/$ IEEE.. NegarCabi Faculty of Electrical and Computer Engineering, Siraz University, Siraz, IRAN, MeranYazdi Department of medical radiation, SaidBeestiUniversity,Teran,IRAN, Moammad Entezarmadi Department of nuclear medicine, Siraz University of Medical Science,Siraz,IRAN, An efficient image fusion metod based on dual tree complex wavelet transform, 03 8t Iranian Conference on Macine Vision and Image Processing (MVIP) 03 IEEE. Copyrigt to IJIRCCE /ijircce