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Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011

Image Fusion Technique using Curvelet Transform Sruthy S1 , Latha Parameswaran2

1 M-TECH Scholar, Department of Computer Science and Engineering, AMRITA Vishwa Vidyapeetham, Coimbatore, sruthy78@gmail.com 2 Professor & Vice Chairperson, Department of Computer Science and Engineering, AMRITA Vishwa Vidyapeetham, Coimbatore, p_ latha@cb.amrita.edu of the same scene taken from different modalities. The output is a fused image which is expected to have a better visual quality than the input images.

Abstractâ&#x20AC;&#x201D;Image Fusion is the process of combining information of two or more images into a single image which can retain all important features of the all original images. Input to fusion is a set of images taken from different modalities of the same scene. Output is an image of better quality; which quality depends on a particular application. The objective of fusion is to generate a resultant image which describes a scene better or even higher than any single image with respect to some relevant properties providing a highly informative image. These fusion techniques are especially beneficial in diagnosing and treating cancer in field of medicine. This paper focuses on the development of a different image fusion method using Curvelet Transform. Experimental results show that the proposed algorithm has a better visual quality than the base methods. Also the result if the fused image has been evaluated using a set of quality metrics.

B. Spectral Domain Fusion Methods All the spatial domain based methods have side effects like reducing the contrast of entire image. But these are much useful and simpler in case of high contrast and bright images. Hence there is a requirement to study the performance of spectral domain based fusion methods. Proposed Method : Curvelet Transform Curvelet transform is a tool for representation of curved shapes in images. The concept of curvelet transform is based on the segmentation of the whole image into small overlapping tiles and then applying ridgelet transform on each tile. It is most suitable to work with medical images. Algorithm for first generation curvelet is given below: 1. Split the input image into 3 subbands using additive wavelet transform. 2. Perform tiling on each of the three subbands 3. Perform Discrete Ridgelet Transform on each of tile on all the subbands.

Keywords- Curvelet Transform, Image Fusion, Image Quality Metrics, SVD

I.INTRODUCTION Any piece of information makes sense only if it conveys the actual content, clarity and quality. Image fusion [1] is concerned with the integration or collection of multiple images, e.g. derived from different sensors, into a single composite image that is more suitable for visual perception or computer-processing tasks. Also a set of image quality metrics have been used to assess the quality of the fused image. Objectives of image fusion include collecting all information from input images, not to introduce artifacts and to reduce mis-registration. This paper is organized as follows: Section II describes the existing simple image fusion techniques and the proposed Curvelet Transform. In Section III various image quality metrics have been discussed. In Section IV experimental results have been discussed. Concluding remarks have been discussed in section V

Subband filtering decomposes image into additive components which are the subbands of the image. In order to decompose image Atrous algorithm is given by where Cj represents the low pass content of the image and Wj represents the high pass content.

Figure 1: The Curvelet Transform on Image

Tiling involves dividing the subbands 1 and 2 of the transformed image into overlapping tiles, resulting in smaller dimensions to transform the curved lines to straight lines thus avoiding the edge effects. Segmentation is performed to approximate curved lines into overlapping tiles. Discrete Ridgelet transform is performed on the segmented tiles. This is done by applying 1D discrete wavelet transform to the slices of the radon transform. Ridgelet transform helps in shape detection. The first generation curvelet transform is more complex involves a series of steps. Due to its complexity, the second generation curvelet is much preferred. Thus Second generation does not use the ridgelet transform, and hence reduces the redundancy.

II. EXISTING IMAGE FUSION TECHNIQUES Based on the literature, image fusion methods can be broadly categorized into spatial and spectral domain methods as well. In [2] the author has discussed that spatial domain fusion methods will produces spatial distortions in fused image; whereas these spatial distortion problems can be well handled by spectral domain methods. A. Spatial Domain Fusion Methods The basic spatial domain methods are described as Averaging Method, Select Maximum Method, Select Minimum Method, Principal Component Analysis Method, IHS transform fusion, Singular Value Decomposition (SVD). In all these methods, the input is a set of two or more images ÂŠ 2011 ACEEE DOI: 02.ACE.2011.02. 191

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Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011 Wrapping Algorithm (In frequency domain): 1) Perform FFT on the original image. 2) Divide FFT into collection of tiles 3) For each tile apply a) Translate tile to the origin. b) Wrap parallelogram shaped support of tile around the rectangle with center as the origin as shown in Figure 2. c) Take inverse FFT of wrapped one d) Add curvelet array to collection of curvelet coefficients.

III. QUANTITATIVE IMAGE QUALITY METRICS Quality is a characteristic that measures perceived image degradation i.e., in comparison with ideal or perfect image. Here we employ Full reference Methods. The metrics used are shown in Table1. IV. EXPERIMENTAL RESULTS Resultant image after fusion by the techniques were discussed here on a set of multifocus image.

Figure 2 : Support of wedge before(left) wrapping and after wrapping

Figure 3: Pair of Input Images

Inverse Wrapping Algorithm: 1. For each curvelet coefficient array a) Take FFT of the array. b) Unwrap rectangular support to original orientation shape. c) Translate it back to the original position d) Store the translated array 2. Add all the translated curvelet arrays 3. Take inverse FFT to reconstruct the image. Thus image fusion is performed using curvelet transformation. The experimental results are discussed in the sections below.

Figure 4: Fused images using different algorithms((1)-(6) are fused images, the methods used from (1) to (6) are: Average, Maximum, used from (1) to (6) are: Average, Maximum, Minimum, PCA, SVD and Curvelet)

TABLE1. TABLE OF QUALITY METRICS READINGS FOR ONE I MAGE SET

From the table1 of Quality metrics for different images it was concluded that the Curvelet based fusion is having better value of PSNR when compared to other fusion methods, which shows it as the better fusion technique. Similarly if we check the MSE value, it is the least in Curvelet. Thus it proves proposed method performs the best.

T ABLE II TABLE FOR R ANK BASED ON METRICS AND VISUAL EVALUATION

A. Visual Evaluation Ten respondents were asked to select the images which were of the best quality according to them. It was inferred that the Curvelet Method fused image was the best from the fused images. Figure5 shows the graph on PSNR value, one of the image quality metrics for eight set of multi focus and multi spectral images. The results shows that the signal to noise ratio is maximum in Curvelet technique based image fusion. Figure 5: Graph of PSNR readings for eight image sets

ÂŠ 2011 ACEEE DOI: 02.ACE.2011.02. 191

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Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011 [5] The Online Resource for Research in Image Fusion www.imagefusion.org [6] Lindsay I Smith, “ A Tutorial on Principal Component Analysis” h t t p:/ / w w w. cs. ot ago. ac. n z/cosc4 5 3 / st u dn en t _ t u t orials/ principal_components.pdf [7] Zhang Zhong, “Investigations on Image Fusion”, PhD Thesis, University of Lehigh, USA. May1999 [8] Shivsubramani Krishnamoorthy, Soman K. P, “Implementation and Comparative Study of Image Fusion Algorithms”, International Journal of Computer Applications, Vol. 19, no. 2, Nov. 2010 [9] Mohd. Shahid, Sumana Gupta, “Novel Masks for multimodality image fusion using DT-CWT”, 9th International Conference on Information Fusion, 2006 [10] C . Sydney, Burrus Ramesh, A. Gopinath and Haitao Guo , Introduction to wavelets and wavelets transforms – A primer , Prentice Hall,1998. [11] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity”, IEEE Transactions on Image Processing, Vol. 13, no. 4, pp.600-612, Apr. 2004 [12] Choi, M., R. Y. Kim, and M. G, Kim, “The curvelet transform for image fusion,” International Society for Photogrammetry and Remote Sensing, ISPRS 2004, Vol. 35, Part B8, 59-64, Istanbul, 2004

V. CONCLUSION Experiments carried out show the fused grayscale images and they were analyzed for their quality, with reference to the original image for different image sets. Based on the quality metrics and the visual quality it was observed that the Curvelet performs well for grayscale images than SVD, Min, Max, Average and IHS Method. This paper can be extended further to include more and more hybrid techniques so that the user has a flexibility to have a choice of fusion techniques. Action plan for future includes, fusion trend utilizing Framelets to explore methods that are highly suitable and applicable to medical images. REFERENCES [1] Wang .Z. J. Ziou, D. Armenakis, C. Li and D . R . Li , “A Comparative analysis of image fusion methods”, IEEE Transactions on Geoscience Remote Sensing, 2005,6,1391-1402. [2] Tania Stathaki, Image fusion: Algorithms and Applications, 2008, pp-27-37, 139-144. [3] Andrews, H, Patterson, C.L.: “Singular Value Decomposition (SVD) for Image Coding,” IEEE Transaction on Communication. Vol. 24 (1976) 425-432 [4] Fusetool - An Image Fusion Toolbox for Matlab 5.x, http:// www.metapix.de/toolbox.htm

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